Overview

Brought to you by YData

Dataset statistics

Number of variables94
Number of observations512
Missing cells43330
Missing cells (%)90.0%
Duplicate rows1
Duplicate rows (%)0.2%
Total size in memory979.2 KiB
Average record size in memory1.9 KiB

Variable types

Numeric20
Categorical35
Text21
Unsupported8
DateTime10

Alerts

privilege_id has constant value "2"Constant
business_phone has constant value "(123)555-0100"Constant
home_phone has constant value "(123)555-0102"Constant
zip_postal_code has constant value "99999"Constant
country_region has constant value "USA"Constant
attachments has constant value ""Constant
tax has constant value "0.0"Constant
shipping has constant value "0.0"Constant
amount_due has constant value "0.0"Constant
discount has constant value "0.0"Constant
ship_zip_postal_code has constant value "99999"Constant
ship_country_region has constant value "USA"Constant
taxes has constant value "0.0"Constant
tax_rate has constant value "0.0"Constant
privilege_name has constant value "Purchase Approvals"Constant
discontinued has constant value "0"Constant
payment_amount has constant value "0.0"Constant
payment_method has constant value "Check"Constant
approved_by has constant value "2"Constant
Dataset has 1 (0.2%) duplicate rowsDuplicates
__table_name__ is highly overall correlated with category and 30 other fieldsHigh correlation
category is highly overall correlated with __table_name__ and 1 other fieldsHigh correlation
city is highly overall correlated with fax_number and 2 other fieldsHigh correlation
created_by is highly overall correlated with __table_name__ and 1 other fieldsHigh correlation
customer_id is highly overall correlated with __table_name__ and 5 other fieldsHigh correlation
default is highly overall correlated with __table_name__High correlation
fax_number is highly overall correlated with __table_name__ and 5 other fieldsHigh correlation
id is highly overall correlated with __table_name__ and 10 other fieldsHigh correlation
inventory_id is highly overall correlated with __table_name__ and 3 other fieldsHigh correlation
job_title is highly overall correlated with __table_name__ and 2 other fieldsHigh correlation
list_price is highly overall correlated with __table_name__ and 2 other fieldsHigh correlation
minimum_reorder_quantity is highly overall correlated with __table_name__ and 2 other fieldsHigh correlation
order_id is highly overall correlated with id and 2 other fieldsHigh correlation
payment_type is highly overall correlated with __table_name__ and 5 other fieldsHigh correlation
posted_to_inventory is highly overall correlated with __table_name__ and 2 other fieldsHigh correlation
purchase_order_id is highly overall correlated with __table_name__ and 3 other fieldsHigh correlation
reorder_level is highly overall correlated with __table_name__ and 2 other fieldsHigh correlation
ship_address is highly overall correlated with __table_name__ and 7 other fieldsHigh correlation
ship_city is highly overall correlated with __table_name__ and 7 other fieldsHigh correlation
ship_name is highly overall correlated with __table_name__ and 7 other fieldsHigh correlation
ship_state_province is highly overall correlated with __table_name__ and 7 other fieldsHigh correlation
shipper_id is highly overall correlated with __table_name__ and 4 other fieldsHigh correlation
shipping_fee is highly overall correlated with id and 4 other fieldsHigh correlation
standard_cost is highly overall correlated with __table_name__ and 2 other fieldsHigh correlation
state_province is highly overall correlated with __table_name__ and 4 other fieldsHigh correlation
status_id is highly overall correlated with __table_name__High correlation
string_id is highly overall correlated with __table_name__High correlation
submitted_by is highly overall correlated with __table_name__ and 1 other fieldsHigh correlation
supplier_id is highly overall correlated with __table_name__High correlation
supplier_ids is highly overall correlated with __table_name__ and 1 other fieldsHigh correlation
target_level is highly overall correlated with __table_name__ and 2 other fieldsHigh correlation
transaction_type is highly overall correlated with __table_name__ and 1 other fieldsHigh correlation
unit_cost is highly overall correlated with __table_name__High correlation
unit_price is highly overall correlated with __table_name__High correlation
web_page is highly overall correlated with __table_name__ and 3 other fieldsHigh correlation
employee_id has 463 (90.4%) missing valuesMissing
privilege_id has 511 (99.8%) missing valuesMissing
id has 68 (13.3%) missing valuesMissing
company has 460 (89.8%) missing valuesMissing
last_name has 463 (90.4%) missing valuesMissing
first_name has 463 (90.4%) missing valuesMissing
email_address has 503 (98.2%) missing valuesMissing
job_title has 463 (90.4%) missing valuesMissing
business_phone has 473 (92.4%) missing valuesMissing
home_phone has 503 (98.2%) missing valuesMissing
mobile_phone has 512 (100.0%) missing valuesMissing
fax_number has 473 (92.4%) missing valuesMissing
address has 470 (91.8%) missing valuesMissing
city has 470 (91.8%) missing valuesMissing
state_province has 470 (91.8%) missing valuesMissing
zip_postal_code has 470 (91.8%) missing valuesMissing
country_region has 470 (91.8%) missing valuesMissing
web_page has 503 (98.2%) missing valuesMissing
notes has 490 (95.7%) missing valuesMissing
attachments has 415 (81.1%) missing valuesMissing
type_name has 508 (99.2%) missing valuesMissing
transaction_type has 410 (80.1%) missing valuesMissing
transaction_created_date has 410 (80.1%) missing valuesMissing
transaction_modified_date has 410 (80.1%) missing valuesMissing
product_id has 297 (58.0%) missing valuesMissing
quantity has 297 (58.0%) missing valuesMissing
purchase_order_id has 442 (86.3%) missing valuesMissing
customer_order_id has 512 (100.0%) missing valuesMissing
comments has 498 (97.3%) missing valuesMissing
order_id has 419 (81.8%) missing valuesMissing
invoice_date has 477 (93.2%) missing valuesMissing
due_date has 512 (100.0%) missing valuesMissing
tax has 477 (93.2%) missing valuesMissing
shipping has 477 (93.2%) missing valuesMissing
amount_due has 477 (93.2%) missing valuesMissing
unit_price has 454 (88.7%) missing valuesMissing
discount has 454 (88.7%) missing valuesMissing
status_id has 378 (73.8%) missing valuesMissing
date_allocated has 512 (100.0%) missing valuesMissing
inventory_id has 413 (80.7%) missing valuesMissing
status has 502 (98.0%) missing valuesMissing
customer_id has 464 (90.6%) missing valuesMissing
order_date has 464 (90.6%) missing valuesMissing
shipped_date has 473 (92.4%) missing valuesMissing
shipper_id has 469 (91.6%) missing valuesMissing
ship_name has 464 (90.6%) missing valuesMissing
ship_address has 464 (90.6%) missing valuesMissing
ship_city has 464 (90.6%) missing valuesMissing
ship_state_province has 464 (90.6%) missing valuesMissing
ship_zip_postal_code has 464 (90.6%) missing valuesMissing
ship_country_region has 464 (90.6%) missing valuesMissing
shipping_fee has 436 (85.2%) missing valuesMissing
taxes has 436 (85.2%) missing valuesMissing
payment_type has 474 (92.6%) missing valuesMissing
paid_date has 474 (92.6%) missing valuesMissing
tax_rate has 464 (90.6%) missing valuesMissing
tax_status_id has 512 (100.0%) missing valuesMissing
status_name has 508 (99.2%) missing valuesMissing
tax_status_name has 510 (99.6%) missing valuesMissing
privilege_name has 511 (99.8%) missing valuesMissing
supplier_ids has 467 (91.2%) missing valuesMissing
product_code has 467 (91.2%) missing valuesMissing
product_name has 467 (91.2%) missing valuesMissing
description has 512 (100.0%) missing valuesMissing
standard_cost has 467 (91.2%) missing valuesMissing
list_price has 467 (91.2%) missing valuesMissing
reorder_level has 467 (91.2%) missing valuesMissing
target_level has 467 (91.2%) missing valuesMissing
quantity_per_unit has 472 (92.2%) missing valuesMissing
discontinued has 467 (91.2%) missing valuesMissing
minimum_reorder_quantity has 482 (94.1%) missing valuesMissing
category has 467 (91.2%) missing valuesMissing
unit_cost has 457 (89.3%) missing valuesMissing
date_received has 469 (91.6%) missing valuesMissing
posted_to_inventory has 457 (89.3%) missing valuesMissing
supplier_id has 484 (94.5%) missing valuesMissing
created_by has 487 (95.1%) missing valuesMissing
submitted_date has 484 (94.5%) missing valuesMissing
creation_date has 484 (94.5%) missing valuesMissing
expected_date has 512 (100.0%) missing valuesMissing
payment_date has 512 (100.0%) missing valuesMissing
payment_amount has 484 (94.5%) missing valuesMissing
payment_method has 510 (99.6%) missing valuesMissing
approved_by has 487 (95.1%) missing valuesMissing
approved_date has 487 (95.1%) missing valuesMissing
submitted_by has 484 (94.5%) missing valuesMissing
group_by has 507 (99.0%) missing valuesMissing
display has 507 (99.0%) missing valuesMissing
title has 507 (99.0%) missing valuesMissing
filter_row_source has 507 (99.0%) missing valuesMissing
default has 507 (99.0%) missing valuesMissing
string_id has 450 (87.9%) missing valuesMissing
string_data has 450 (87.9%) missing valuesMissing
mobile_phone is an unsupported type, check if it needs cleaning or further analysisUnsupported
customer_order_id is an unsupported type, check if it needs cleaning or further analysisUnsupported
due_date is an unsupported type, check if it needs cleaning or further analysisUnsupported
date_allocated is an unsupported type, check if it needs cleaning or further analysisUnsupported
tax_status_id is an unsupported type, check if it needs cleaning or further analysisUnsupported
description is an unsupported type, check if it needs cleaning or further analysisUnsupported
expected_date is an unsupported type, check if it needs cleaning or further analysisUnsupported
payment_date is an unsupported type, check if it needs cleaning or further analysisUnsupported
shipping_fee has 40 (7.8%) zerosZeros

Reproduction

Analysis started2025-10-30 16:21:44.370986
Analysis finished2025-10-30 16:23:00.401455
Duration1 minute and 16.03 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

employee_id
Real number (ℝ)

Missing 

Distinct8
Distinct (%)16.3%
Missing463
Missing (%)90.4%
Infinite0
Infinite (%)0.0%
Mean4.4081633
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2025-10-30T18:23:00.479570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q37
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.0339631
Coefficient of variation (CV)0.68826014
Kurtosis-1.3286551
Mean4.4081633
Median Absolute Deviation (MAD)3
Skewness0.42747153
Sum216
Variance9.204932
MonotonicityNot monotonic
2025-10-30T18:23:00.580873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
112
 
2.3%
910
 
2.0%
48
 
1.6%
36
 
1.2%
25
 
1.0%
64
 
0.8%
82
 
0.4%
72
 
0.4%
(Missing)463
90.4%
ValueCountFrequency (%)
112
2.3%
25
1.0%
36
1.2%
48
1.6%
64
 
0.8%
72
 
0.4%
82
 
0.4%
910
2.0%
ValueCountFrequency (%)
910
2.0%
82
 
0.4%
72
 
0.4%
64
 
0.8%
48
1.6%
36
1.2%
25
1.0%
112
2.3%

privilege_id
Categorical

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing511
Missing (%)99.8%
Memory size28.1 KiB
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row2

Common Values

ValueCountFrequency (%)
21
 
0.2%
(Missing)511
99.8%

Length

2025-10-30T18:23:00.664356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-30T18:23:00.738537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
21
100.0%

Most occurring characters

ValueCountFrequency (%)
21
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)1
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
21
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
21
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
21
100.0%

__table_name__
Categorical

High correlation 

Distinct20
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size35.4 KiB
inventory_transactions
102 
strings
62 
order_details
58 
purchase_order_details
55 
orders
48 
Other values (15)
187 

Length

Max length27
Median length22
Mean length13.509766
Min length6

Characters and Unicode

Total characters6917
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.4%

Sample

1st rowemployee_privileges
2nd rowemployees
3rd rowemployees
4th rowemployees
5th rowemployees

Common Values

ValueCountFrequency (%)
inventory_transactions102
19.9%
strings62
12.1%
order_details58
11.3%
purchase_order_details55
10.7%
orders48
9.4%
products45
8.8%
invoices35
 
6.8%
customer30
 
5.9%
purchase_orders28
 
5.5%
suppliers10
 
2.0%
Other values (10)39
 
7.6%

Length

2025-10-30T18:23:00.855782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
inventory_transactions102
19.9%
strings62
12.1%
order_details58
11.3%
purchase_order_details55
10.7%
orders48
9.4%
products45
8.8%
invoices35
 
6.8%
customer30
 
5.9%
purchase_orders28
 
5.5%
suppliers10
 
2.0%
Other values (10)39
 
7.6%

Most occurring characters

ValueCountFrequency (%)
r871
12.6%
s818
11.8%
e643
9.3%
t617
8.9%
o542
7.8%
n521
7.5%
i480
 
6.9%
a441
 
6.4%
d369
 
5.3%
_340
 
4.9%
Other values (10)1275
18.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)6917
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r871
12.6%
s818
11.8%
e643
9.3%
t617
8.9%
o542
7.8%
n521
7.5%
i480
 
6.9%
a441
 
6.4%
d369
 
5.3%
_340
 
4.9%
Other values (10)1275
18.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)6917
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r871
12.6%
s818
11.8%
e643
9.3%
t617
8.9%
o542
7.8%
n521
7.5%
i480
 
6.9%
a441
 
6.4%
d369
 
5.3%
_340
 
4.9%
Other values (10)1275
18.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)6917
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r871
12.6%
s818
11.8%
e643
9.3%
t617
8.9%
o542
7.8%
n521
7.5%
i480
 
6.9%
a441
 
6.4%
d369
 
5.3%
_340
 
4.9%
Other values (10)1275
18.4%

id
Real number (ℝ)

High correlation  Missing 

Distinct198
Distinct (%)44.6%
Missing68
Missing (%)13.3%
Infinite0
Infinite (%)0.0%
Mean81.867117
Minimum0
Maximum295
Zeros4
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2025-10-30T18:23:00.992096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q129
median63
Q399
95-th percentile269.85
Maximum295
Range295
Interquartile range (IQR)70

Descriptive statistics

Standard deviation78.011679
Coefficient of variation (CV)0.95290614
Kurtosis1.2419314
Mean81.867117
Median Absolute Deviation (MAD)35
Skewness1.4472114
Sum36349
Variance6085.822
MonotonicityNot monotonic
2025-10-30T18:23:01.110253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
111
 
2.1%
39
 
1.8%
29
 
1.8%
46
 
1.2%
56
 
1.2%
75
 
1.0%
65
 
1.0%
85
 
1.0%
364
 
0.8%
94
 
0.8%
Other values (188)380
74.2%
(Missing)68
 
13.3%
ValueCountFrequency (%)
04
 
0.8%
111
2.1%
29
1.8%
39
1.8%
46
1.2%
56
1.2%
65
1.0%
75
1.0%
85
1.0%
94
 
0.8%
ValueCountFrequency (%)
2951
0.2%
2941
0.2%
2931
0.2%
2921
0.2%
2901
0.2%
2891
0.2%
2881
0.2%
2861
0.2%
2851
0.2%
2831
0.2%

company
Text

Missing 

Distinct43
Distinct (%)82.7%
Missing460
Missing (%)89.8%
Memory size18.0 KiB
2025-10-30T18:23:01.256605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length18
Median length9
Mean length11.153846
Min length9

Characters and Unicode

Total characters580
Distinct characters44
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41 ?
Unique (%)78.8%

Sample

1st rowNorthwind Traders
2nd rowNorthwind Traders
3rd rowNorthwind Traders
4th rowNorthwind Traders
5th rowNorthwind Traders
ValueCountFrequency (%)
company33
30.8%
supplier10
 
9.3%
traders9
 
8.4%
northwind9
 
8.4%
a4
 
3.7%
shipping3
 
2.8%
c3
 
2.8%
b3
 
2.8%
d2
 
1.9%
f2
 
1.9%
Other values (24)29
27.1%
2025-10-30T18:23:01.474867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
p59
 
10.2%
55
 
9.5%
n45
 
7.8%
o42
 
7.2%
a42
 
7.2%
C38
 
6.6%
r37
 
6.4%
y33
 
5.7%
m33
 
5.7%
i25
 
4.3%
Other values (34)171
29.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)580
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
p59
 
10.2%
55
 
9.5%
n45
 
7.8%
o42
 
7.2%
a42
 
7.2%
C38
 
6.6%
r37
 
6.4%
y33
 
5.7%
m33
 
5.7%
i25
 
4.3%
Other values (34)171
29.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)580
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
p59
 
10.2%
55
 
9.5%
n45
 
7.8%
o42
 
7.2%
a42
 
7.2%
C38
 
6.6%
r37
 
6.4%
y33
 
5.7%
m33
 
5.7%
i25
 
4.3%
Other values (34)171
29.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)580
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
p59
 
10.2%
55
 
9.5%
n45
 
7.8%
o42
 
7.2%
a42
 
7.2%
C38
 
6.6%
r37
 
6.4%
y33
 
5.7%
m33
 
5.7%
i25
 
4.3%
Other values (34)171
29.5%

last_name
Text

Missing 

Distinct46
Distinct (%)93.9%
Missing463
Missing (%)90.4%
Memory size17.7 KiB
2025-10-30T18:23:01.612064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length20
Median length13
Mean length7.0204082
Min length2

Characters and Unicode

Total characters344
Distinct characters48
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43 ?
Unique (%)87.8%

Sample

1st rowGiussani
2nd rowThorpe
3rd rowZare
4th rowFreehafer
5th rowNeipper
ValueCountFrequency (%)
andersen2
 
3.9%
lee2
 
3.9%
bedecs2
 
3.9%
freehafer1
 
2.0%
zare1
 
2.0%
giussani1
 
2.0%
kotas1
 
2.0%
sergienko1
 
2.0%
hellung-larsen1
 
2.0%
cencini1
 
2.0%
Other values (38)38
74.5%
2025-10-30T18:23:01.902598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e47
 
13.7%
a31
 
9.0%
n27
 
7.8%
r25
 
7.3%
s20
 
5.8%
o19
 
5.5%
i18
 
5.2%
l14
 
4.1%
d12
 
3.5%
c10
 
2.9%
Other values (38)121
35.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)344
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e47
 
13.7%
a31
 
9.0%
n27
 
7.8%
r25
 
7.3%
s20
 
5.8%
o19
 
5.5%
i18
 
5.2%
l14
 
4.1%
d12
 
3.5%
c10
 
2.9%
Other values (38)121
35.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)344
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e47
 
13.7%
a31
 
9.0%
n27
 
7.8%
r25
 
7.3%
s20
 
5.8%
o19
 
5.5%
i18
 
5.2%
l14
 
4.1%
d12
 
3.5%
c10
 
2.9%
Other values (38)121
35.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)344
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e47
 
13.7%
a31
 
9.0%
n27
 
7.8%
r25
 
7.3%
s20
 
5.8%
o19
 
5.5%
i18
 
5.2%
l14
 
4.1%
d12
 
3.5%
c10
 
2.9%
Other values (38)121
35.2%

first_name
Text

Missing 

Distinct46
Distinct (%)93.9%
Missing463
Missing (%)90.4%
Memory size17.6 KiB
2025-10-30T18:23:02.053001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length13
Median length9
Mean length6.4285714
Min length3

Characters and Unicode

Total characters315
Distinct characters44
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43 ?
Unique (%)87.8%

Sample

1st rowLaura
2nd rowSteven
3rd rowRobert
4th rowNancy
5th rowMichael
ValueCountFrequency (%)
michael2
 
3.8%
john2
 
3.8%
anna2
 
3.8%
elizabeth2
 
3.8%
robert1
 
1.9%
laura1
 
1.9%
mariya1
 
1.9%
andrew1
 
1.9%
steven1
 
1.9%
anne1
 
1.9%
Other values (39)39
73.6%
2025-10-30T18:23:02.703053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a39
 
12.4%
n37
 
11.7%
e31
 
9.8%
i23
 
7.3%
r20
 
6.3%
o16
 
5.1%
l14
 
4.4%
t13
 
4.1%
h11
 
3.5%
A10
 
3.2%
Other values (34)101
32.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)315
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a39
 
12.4%
n37
 
11.7%
e31
 
9.8%
i23
 
7.3%
r20
 
6.3%
o16
 
5.1%
l14
 
4.4%
t13
 
4.1%
h11
 
3.5%
A10
 
3.2%
Other values (34)101
32.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)315
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a39
 
12.4%
n37
 
11.7%
e31
 
9.8%
i23
 
7.3%
r20
 
6.3%
o16
 
5.1%
l14
 
4.4%
t13
 
4.1%
h11
 
3.5%
A10
 
3.2%
Other values (34)101
32.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)315
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a39
 
12.4%
n37
 
11.7%
e31
 
9.8%
i23
 
7.3%
r20
 
6.3%
o16
 
5.1%
l14
 
4.4%
t13
 
4.1%
h11
 
3.5%
A10
 
3.2%
Other values (34)101
32.1%

email_address
Text

Missing 

Distinct9
Distinct (%)100.0%
Missing503
Missing (%)98.2%
Memory size16.6 KiB
2025-10-30T18:23:02.795099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length28
Median length27
Mean length26.333333
Min length24

Characters and Unicode

Total characters237
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)100.0%

Sample

1st rowlaura@northwindtraders.com
2nd rowsteven@northwindtraders.com
3rd rowrobert@northwindtraders.com
4th rownancy@northwindtraders.com
5th rowmichael@northwindtraders.com
ValueCountFrequency (%)
laura@northwindtraders.com1
11.1%
steven@northwindtraders.com1
11.1%
robert@northwindtraders.com1
11.1%
nancy@northwindtraders.com1
11.1%
michael@northwindtraders.com1
11.1%
jan@northwindtraders.com1
11.1%
mariya@northwindtraders.com1
11.1%
andrew@northwindtraders.com1
11.1%
anne@northwindtraders.com1
11.1%
2025-10-30T18:23:03.035970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r32
13.5%
n25
10.5%
t20
 
8.4%
d19
 
8.0%
o19
 
8.0%
a18
 
7.6%
e15
 
6.3%
c11
 
4.6%
i11
 
4.6%
m11
 
4.6%
Other values (11)56
23.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)237
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r32
13.5%
n25
10.5%
t20
 
8.4%
d19
 
8.0%
o19
 
8.0%
a18
 
7.6%
e15
 
6.3%
c11
 
4.6%
i11
 
4.6%
m11
 
4.6%
Other values (11)56
23.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)237
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r32
13.5%
n25
10.5%
t20
 
8.4%
d19
 
8.0%
o19
 
8.0%
a18
 
7.6%
e15
 
6.3%
c11
 
4.6%
i11
 
4.6%
m11
 
4.6%
Other values (11)56
23.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)237
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r32
13.5%
n25
10.5%
t20
 
8.4%
d19
 
8.0%
o19
 
8.0%
a18
 
7.6%
e15
 
6.3%
c11
 
4.6%
i11
 
4.6%
m11
 
4.6%
Other values (11)56
23.6%

job_title
Categorical

High correlation  Missing 

Distinct12
Distinct (%)24.5%
Missing463
Missing (%)90.4%
Memory size29.0 KiB
Purchasing Manager
13 
Sales Representative
Owner
Sales Manager
Purchasing Representative
Other values (7)

Length

Max length25
Median length21
Mean length16.857143
Min length5

Characters and Unicode

Total characters826
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)10.2%

Sample

1st rowSales Coordinator
2nd rowSales Manager
3rd rowSales Representative
4th rowSales Representative
5th rowSales Representative

Common Values

ValueCountFrequency (%)
Purchasing Manager13
 
2.5%
Sales Representative8
 
1.6%
Owner7
 
1.4%
Sales Manager6
 
1.2%
Purchasing Representative6
 
1.2%
Marketing Manager2
 
0.4%
Accounting Assistant2
 
0.4%
Sales Coordinator1
 
0.2%
Vice President, Sales1
 
0.2%
Marketing Assistant1
 
0.2%
Other values (2)2
 
0.4%
(Missing)463
90.4%

Length

2025-10-30T18:23:03.125208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
manager22
23.9%
purchasing20
21.7%
sales16
17.4%
representative14
15.2%
owner7
 
7.6%
assistant4
 
4.3%
marketing3
 
3.3%
accounting3
 
3.3%
coordinator1
 
1.1%
vice1
 
1.1%

Most occurring characters

ValueCountFrequency (%)
e107
13.0%
a102
12.3%
n78
 
9.4%
r69
 
8.4%
s63
 
7.6%
g48
 
5.8%
i47
 
5.7%
t44
 
5.3%
43
 
5.2%
c27
 
3.3%
Other values (18)198
24.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)826
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e107
13.0%
a102
12.3%
n78
 
9.4%
r69
 
8.4%
s63
 
7.6%
g48
 
5.8%
i47
 
5.7%
t44
 
5.3%
43
 
5.2%
c27
 
3.3%
Other values (18)198
24.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)826
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e107
13.0%
a102
12.3%
n78
 
9.4%
r69
 
8.4%
s63
 
7.6%
g48
 
5.8%
i47
 
5.7%
t44
 
5.3%
43
 
5.2%
c27
 
3.3%
Other values (18)198
24.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)826
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e107
13.0%
a102
12.3%
n78
 
9.4%
r69
 
8.4%
s63
 
7.6%
g48
 
5.8%
i47
 
5.7%
t44
 
5.3%
43
 
5.2%
c27
 
3.3%
Other values (18)198
24.0%

business_phone
Categorical

Constant  Missing 

Distinct1
Distinct (%)2.6%
Missing473
Missing (%)92.4%
Memory size28.7 KiB
(123)555-0100
39 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters507
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row(123)555-0100
2nd row(123)555-0100
3rd row(123)555-0100
4th row(123)555-0100
5th row(123)555-0100

Common Values

ValueCountFrequency (%)
(123)555-010039
 
7.6%
(Missing)473
92.4%

Length

2025-10-30T18:23:03.216462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-30T18:23:03.265600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
123)555-010039
100.0%

Most occurring characters

ValueCountFrequency (%)
0117
23.1%
5117
23.1%
178
15.4%
(39
 
7.7%
339
 
7.7%
239
 
7.7%
)39
 
7.7%
-39
 
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0117
23.1%
5117
23.1%
178
15.4%
(39
 
7.7%
339
 
7.7%
239
 
7.7%
)39
 
7.7%
-39
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0117
23.1%
5117
23.1%
178
15.4%
(39
 
7.7%
339
 
7.7%
239
 
7.7%
)39
 
7.7%
-39
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0117
23.1%
5117
23.1%
178
15.4%
(39
 
7.7%
339
 
7.7%
239
 
7.7%
)39
 
7.7%
-39
 
7.7%

home_phone
Categorical

Constant  Missing 

Distinct1
Distinct (%)11.1%
Missing503
Missing (%)98.2%
Memory size28.2 KiB
(123)555-0102

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters117
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row(123)555-0102
2nd row(123)555-0102
3rd row(123)555-0102
4th row(123)555-0102
5th row(123)555-0102

Common Values

ValueCountFrequency (%)
(123)555-01029
 
1.8%
(Missing)503
98.2%

Length

2025-10-30T18:23:03.321192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-30T18:23:03.367650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
123)555-01029
100.0%

Most occurring characters

ValueCountFrequency (%)
527
23.1%
118
15.4%
018
15.4%
218
15.4%
39
 
7.7%
(9
 
7.7%
)9
 
7.7%
-9
 
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)117
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
527
23.1%
118
15.4%
018
15.4%
218
15.4%
39
 
7.7%
(9
 
7.7%
)9
 
7.7%
-9
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)117
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
527
23.1%
118
15.4%
018
15.4%
218
15.4%
39
 
7.7%
(9
 
7.7%
)9
 
7.7%
-9
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)117
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
527
23.1%
118
15.4%
018
15.4%
218
15.4%
39
 
7.7%
(9
 
7.7%
)9
 
7.7%
-9
 
7.7%

mobile_phone
Unsupported

Missing  Rejected  Unsupported 

Missing512
Missing (%)100.0%
Memory size4.1 KiB

fax_number
Categorical

High correlation  Missing 

Distinct2
Distinct (%)5.1%
Missing473
Missing (%)92.4%
Memory size28.7 KiB
(123)555-0101
30 
(123)555-0103

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters507
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row(123)555-0103
2nd row(123)555-0103
3rd row(123)555-0103
4th row(123)555-0103
5th row(123)555-0103

Common Values

ValueCountFrequency (%)
(123)555-010130
 
5.9%
(123)555-01039
 
1.8%
(Missing)473
92.4%

Length

2025-10-30T18:23:03.441077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-30T18:23:03.519499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
123)555-010130
76.9%
123)555-01039
 
23.1%

Most occurring characters

ValueCountFrequency (%)
5117
23.1%
1108
21.3%
078
15.4%
348
9.5%
239
 
7.7%
(39
 
7.7%
)39
 
7.7%
-39
 
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5117
23.1%
1108
21.3%
078
15.4%
348
9.5%
239
 
7.7%
(39
 
7.7%
)39
 
7.7%
-39
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5117
23.1%
1108
21.3%
078
15.4%
348
9.5%
239
 
7.7%
(39
 
7.7%
)39
 
7.7%
-39
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5117
23.1%
1108
21.3%
078
15.4%
348
9.5%
239
 
7.7%
(39
 
7.7%
)39
 
7.7%
-39
 
7.7%

address
Text

Missing 

Distinct39
Distinct (%)92.9%
Missing470
Missing (%)91.8%
Memory size17.7 KiB
2025-10-30T18:23:03.607918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length15
Median length14
Mean length14.47619
Min length14

Characters and Unicode

Total characters608
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique37 ?
Unique (%)88.1%

Sample

1st row123 8th Avenue
2nd row123 5th Avenue
3rd row123 7th Avenue
4th row123 1st Avenue
5th row123 6th Avenue
ValueCountFrequency (%)
street33
26.2%
12325
19.8%
78911
 
8.7%
avenue9
 
7.1%
4566
 
4.8%
any3
 
2.4%
1st3
 
2.4%
5th2
 
1.6%
7th2
 
1.6%
8th2
 
1.6%
Other values (25)30
23.8%
2025-10-30T18:23:03.812464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t101
16.6%
e84
13.8%
84
13.8%
140
 
6.6%
239
 
6.4%
r35
 
5.8%
S33
 
5.4%
h32
 
5.3%
329
 
4.8%
915
 
2.5%
Other values (13)116
19.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)608
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t101
16.6%
e84
13.8%
84
13.8%
140
 
6.6%
239
 
6.4%
r35
 
5.8%
S33
 
5.4%
h32
 
5.3%
329
 
4.8%
915
 
2.5%
Other values (13)116
19.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)608
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t101
16.6%
e84
13.8%
84
13.8%
140
 
6.6%
239
 
6.4%
r35
 
5.8%
S33
 
5.4%
h32
 
5.3%
329
 
4.8%
915
 
2.5%
Other values (13)116
19.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)608
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t101
16.6%
e84
13.8%
84
13.8%
140
 
6.6%
239
 
6.4%
r35
 
5.8%
S33
 
5.4%
h32
 
5.3%
329
 
4.8%
915
 
2.5%
Other values (13)116
19.1%

city
Categorical

High correlation  Missing 

Distinct19
Distinct (%)45.2%
Missing470
Missing (%)91.8%
Memory size28.5 KiB
Seattle
Memphis
Redmond
New York
 
2
Los Angelas
 
2
Other values (14)
23 

Length

Max length14
Median length13
Mean length7.9761905
Min length5

Characters and Unicode

Total characters335
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)11.9%

Sample

1st rowRedmond
2nd rowSeattle
3rd rowSeattle
4th rowSeattle
5th rowRedmond

Common Values

ValueCountFrequency (%)
Seattle7
 
1.4%
Memphis5
 
1.0%
Redmond3
 
0.6%
New York2
 
0.4%
Los Angelas2
 
0.4%
Miami2
 
0.4%
Denver2
 
0.4%
Las Vegas2
 
0.4%
Minneapolis2
 
0.4%
Boston2
 
0.4%
Other values (9)13
 
2.5%
(Missing)470
91.8%

Length

2025-10-30T18:23:03.936913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
seattle7
 
13.2%
memphis5
 
9.4%
redmond3
 
5.7%
new2
 
3.8%
york2
 
3.8%
los2
 
3.8%
angelas2
 
3.8%
miami2
 
3.8%
denver2
 
3.8%
las2
 
3.8%
Other values (15)24
45.3%

Most occurring characters

ValueCountFrequency (%)
e44
 
13.1%
a30
 
9.0%
t22
 
6.6%
l22
 
6.6%
i22
 
6.6%
o21
 
6.3%
n19
 
5.7%
s19
 
5.7%
M11
 
3.3%
11
 
3.3%
Other values (26)114
34.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)335
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e44
 
13.1%
a30
 
9.0%
t22
 
6.6%
l22
 
6.6%
i22
 
6.6%
o21
 
6.3%
n19
 
5.7%
s19
 
5.7%
M11
 
3.3%
11
 
3.3%
Other values (26)114
34.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)335
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e44
 
13.1%
a30
 
9.0%
t22
 
6.6%
l22
 
6.6%
i22
 
6.6%
o21
 
6.3%
n19
 
5.7%
s19
 
5.7%
M11
 
3.3%
11
 
3.3%
Other values (26)114
34.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)335
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e44
 
13.1%
a30
 
9.0%
t22
 
6.6%
l22
 
6.6%
i22
 
6.6%
o21
 
6.3%
n19
 
5.7%
s19
 
5.7%
M11
 
3.3%
11
 
3.3%
Other values (26)114
34.0%

state_province
Categorical

High correlation  Missing 

Distinct15
Distinct (%)35.7%
Missing470
Missing (%)91.8%
Memory size28.2 KiB
WA
12 
TN
CA
CO
FL
Other values (10)
18 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters84
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)4.8%

Sample

1st rowWA
2nd rowWA
3rd rowWA
4th rowWA
5th rowWA

Common Values

ValueCountFrequency (%)
WA12
 
2.3%
TN5
 
1.0%
CA3
 
0.6%
CO2
 
0.4%
FL2
 
0.4%
MA2
 
0.4%
IL2
 
0.4%
NY2
 
0.4%
OR2
 
0.4%
MN2
 
0.4%
Other values (5)8
 
1.6%
(Missing)470
91.8%

Length

2025-10-30T18:23:04.039164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
wa12
28.6%
tn5
11.9%
ca3
 
7.1%
co2
 
4.8%
fl2
 
4.8%
ma2
 
4.8%
il2
 
4.8%
ny2
 
4.8%
or2
 
4.8%
mn2
 
4.8%
Other values (5)8
19.0%

Most occurring characters

ValueCountFrequency (%)
A17
20.2%
W14
16.7%
N11
13.1%
T7
8.3%
I6
 
7.1%
C5
 
6.0%
L4
 
4.8%
O4
 
4.8%
M4
 
4.8%
F2
 
2.4%
Other values (6)10
11.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)84
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A17
20.2%
W14
16.7%
N11
13.1%
T7
8.3%
I6
 
7.1%
C5
 
6.0%
L4
 
4.8%
O4
 
4.8%
M4
 
4.8%
F2
 
2.4%
Other values (6)10
11.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)84
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A17
20.2%
W14
16.7%
N11
13.1%
T7
8.3%
I6
 
7.1%
C5
 
6.0%
L4
 
4.8%
O4
 
4.8%
M4
 
4.8%
F2
 
2.4%
Other values (6)10
11.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)84
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A17
20.2%
W14
16.7%
N11
13.1%
T7
8.3%
I6
 
7.1%
C5
 
6.0%
L4
 
4.8%
O4
 
4.8%
M4
 
4.8%
F2
 
2.4%
Other values (6)10
11.9%

zip_postal_code
Categorical

Constant  Missing 

Distinct1
Distinct (%)2.4%
Missing470
Missing (%)91.8%
Memory size28.4 KiB
99999
42 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters210
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row99999
2nd row99999
3rd row99999
4th row99999
5th row99999

Common Values

ValueCountFrequency (%)
9999942
 
8.2%
(Missing)470
91.8%

Length

2025-10-30T18:23:04.135291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-30T18:23:04.182116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
9999942
100.0%

Most occurring characters

ValueCountFrequency (%)
9210
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)210
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9210
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)210
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9210
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)210
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9210
100.0%

country_region
Categorical

Constant  Missing 

Distinct1
Distinct (%)2.4%
Missing470
Missing (%)91.8%
Memory size28.3 KiB
USA
42 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters126
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUSA
2nd rowUSA
3rd rowUSA
4th rowUSA
5th rowUSA

Common Values

ValueCountFrequency (%)
USA42
 
8.2%
(Missing)470
91.8%

Length

2025-10-30T18:23:04.269534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-30T18:23:04.324433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
usa42
100.0%

Most occurring characters

ValueCountFrequency (%)
U42
33.3%
S42
33.3%
A42
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)126
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U42
33.3%
S42
33.3%
A42
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)126
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U42
33.3%
S42
33.3%
A42
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)126
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U42
33.3%
S42
33.3%
A42
33.3%

web_page
Categorical

High correlation  Missing 

Distinct2
Distinct (%)22.2%
Missing503
Missing (%)98.2%
Memory size28.6 KiB
http://northwindtraders.com#http://northwindtraders.com/#
#http://northwindtraders.com#

Length

Max length57
Median length57
Mean length53.888889
Min length29

Characters and Unicode

Total characters485
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)11.1%

Sample

1st rowhttp://northwindtraders.com#http://northwindtraders.com/#
2nd rowhttp://northwindtraders.com#http://northwindtraders.com/#
3rd rowhttp://northwindtraders.com#http://northwindtraders.com/#
4th row#http://northwindtraders.com#
5th rowhttp://northwindtraders.com#http://northwindtraders.com/#

Common Values

ValueCountFrequency (%)
http://northwindtraders.com#http://northwindtraders.com/#8
 
1.6%
#http://northwindtraders.com#1
 
0.2%
(Missing)503
98.2%

Length

2025-10-30T18:23:04.386057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-30T18:23:04.446899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
http://northwindtraders.com#http://northwindtraders.com8
88.9%
http://northwindtraders.com1
 
11.1%

Most occurring characters

ValueCountFrequency (%)
t68
14.0%
r51
 
10.5%
/42
 
8.7%
h34
 
7.0%
o34
 
7.0%
n34
 
7.0%
d34
 
7.0%
#18
 
3.7%
p17
 
3.5%
:17
 
3.5%
Other values (8)136
28.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)485
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t68
14.0%
r51
 
10.5%
/42
 
8.7%
h34
 
7.0%
o34
 
7.0%
n34
 
7.0%
d34
 
7.0%
#18
 
3.7%
p17
 
3.5%
:17
 
3.5%
Other values (8)136
28.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)485
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t68
14.0%
r51
 
10.5%
/42
 
8.7%
h34
 
7.0%
o34
 
7.0%
n34
 
7.0%
d34
 
7.0%
#18
 
3.7%
p17
 
3.5%
:17
 
3.5%
Other values (8)136
28.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)485
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t68
14.0%
r51
 
10.5%
/42
 
8.7%
h34
 
7.0%
o34
 
7.0%
n34
 
7.0%
d34
 
7.0%
#18
 
3.7%
p17
 
3.5%
:17
 
3.5%
Other values (8)136
28.0%

notes
Text

Missing 

Distinct20
Distinct (%)90.9%
Missing490
Missing (%)95.7%
Memory size17.6 KiB
2025-10-30T18:23:04.565231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length119
Median length37
Mean length45.772727
Min length24

Characters and Unicode

Total characters1007
Distinct characters43
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)81.8%

Sample

1st rowReads and writes French.
2nd rowJoined the company as a sales representative and was promoted to sales manager. Fluent in French.
3rd rowFluent in Japanese and can read and write French, Portuguese, and Spanish.
4th rowWas hired as a sales associate and was promoted to sales representative.
5th rowJoined the company as a sales representative, was promoted to sales manager and was then named vice president of sales.
ValueCountFrequency (%)
purchase16
 
9.7%
generated16
 
9.7%
based16
 
9.7%
on16
 
9.7%
order16
 
9.7%
and8
 
4.8%
sales7
 
4.2%
was5
 
3.0%
french4
 
2.4%
a3
 
1.8%
Other values (40)58
35.2%
2025-10-30T18:23:04.818444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e147
14.6%
144
14.3%
a93
 
9.2%
r86
 
8.5%
n66
 
6.6%
d66
 
6.6%
s65
 
6.5%
t39
 
3.9%
o32
 
3.2%
c25
 
2.5%
Other values (33)244
24.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)1007
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e147
14.6%
144
14.3%
a93
 
9.2%
r86
 
8.5%
n66
 
6.6%
d66
 
6.6%
s65
 
6.5%
t39
 
3.9%
o32
 
3.2%
c25
 
2.5%
Other values (33)244
24.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1007
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e147
14.6%
144
14.3%
a93
 
9.2%
r86
 
8.5%
n66
 
6.6%
d66
 
6.6%
s65
 
6.5%
t39
 
3.9%
o32
 
3.2%
c25
 
2.5%
Other values (33)244
24.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1007
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e147
14.6%
144
14.3%
a93
 
9.2%
r86
 
8.5%
n66
 
6.6%
d66
 
6.6%
s65
 
6.5%
t39
 
3.9%
o32
 
3.2%
c25
 
2.5%
Other values (33)244
24.2%

attachments
Categorical

Constant  Missing 

Distinct1
Distinct (%)1.0%
Missing415
Missing (%)81.1%
Memory size28.2 KiB
97 

Length

Max length0
Median length0
Mean length0
Min length0

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
97
 
18.9%
(Missing)415
81.1%

Length

2025-10-30T18:23:04.958507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-30T18:23:05.026192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

type_name
Text

Missing 

Distinct4
Distinct (%)100.0%
Missing508
Missing (%)99.2%
Memory size16.2 KiB
2025-10-30T18:23:05.091403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length9
Median length6
Mean length6.25
Min length4

Characters and Unicode

Total characters25
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)100.0%

Sample

1st rowPurchased
2nd rowSold
3rd rowOn Hold
4th rowWaste
ValueCountFrequency (%)
purchased1
20.0%
sold1
20.0%
on1
20.0%
hold1
20.0%
waste1
20.0%
2025-10-30T18:23:05.268633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
d3
 
12.0%
a2
 
8.0%
l2
 
8.0%
o2
 
8.0%
s2
 
8.0%
e2
 
8.0%
u1
 
4.0%
P1
 
4.0%
h1
 
4.0%
c1
 
4.0%
Other values (8)8
32.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)25
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d3
 
12.0%
a2
 
8.0%
l2
 
8.0%
o2
 
8.0%
s2
 
8.0%
e2
 
8.0%
u1
 
4.0%
P1
 
4.0%
h1
 
4.0%
c1
 
4.0%
Other values (8)8
32.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)25
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d3
 
12.0%
a2
 
8.0%
l2
 
8.0%
o2
 
8.0%
s2
 
8.0%
e2
 
8.0%
u1
 
4.0%
P1
 
4.0%
h1
 
4.0%
c1
 
4.0%
Other values (8)8
32.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)25
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d3
 
12.0%
a2
 
8.0%
l2
 
8.0%
o2
 
8.0%
s2
 
8.0%
e2
 
8.0%
u1
 
4.0%
P1
 
4.0%
h1
 
4.0%
c1
 
4.0%
Other values (8)8
32.0%

transaction_type
Categorical

High correlation  Missing 

Distinct3
Distinct (%)2.9%
Missing410
Missing (%)80.1%
Memory size28.3 KiB
2
49 
1
43 
3
10 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters102
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
249
 
9.6%
143
 
8.4%
310
 
2.0%
(Missing)410
80.1%

Length

2025-10-30T18:23:05.377953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-30T18:23:05.433061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
249
48.0%
143
42.2%
310
 
9.8%

Most occurring characters

ValueCountFrequency (%)
249
48.0%
143
42.2%
310
 
9.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)102
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
249
48.0%
143
42.2%
310
 
9.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)102
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
249
48.0%
143
42.2%
310
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)102
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
249
48.0%
143
42.2%
310
 
9.8%
Distinct102
Distinct (%)100.0%
Missing410
Missing (%)80.1%
Memory size4.1 KiB
Minimum2006-03-22 16:02:28
Maximum2006-04-25 17:04:05
Invalid dates0
Invalid dates (%)0.0%
2025-10-30T18:23:05.549093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-30T18:23:05.748057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct88
Distinct (%)86.3%
Missing410
Missing (%)80.1%
Memory size4.1 KiB
Minimum2006-03-22 16:02:28
Maximum2006-04-25 17:04:57
Invalid dates0
Invalid dates (%)0.0%
2025-10-30T18:23:05.936445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-30T18:23:06.099251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

product_id
Real number (ℝ)

Missing 

Distinct29
Distinct (%)13.5%
Missing297
Missing (%)58.0%
Infinite0
Infinite (%)0.0%
Mean37.637209
Minimum1
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2025-10-30T18:23:06.254946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q117
median41
Q352
95-th percentile81
Maximum85
Range84
Interquartile range (IQR)35

Descriptive statistics

Standard deviation25.108708
Coefficient of variation (CV)0.66712459
Kurtosis-1.0386113
Mean37.637209
Median Absolute Deviation (MAD)22
Skewness0.21449306
Sum8092
Variance630.44721
MonotonicityNot monotonic
2025-10-30T18:23:06.366446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
1917
 
3.3%
3417
 
3.3%
4317
 
3.3%
4814
 
2.7%
4114
 
2.7%
812
 
2.3%
8111
 
2.1%
8010
 
2.0%
409
 
1.8%
729
 
1.8%
Other values (19)85
 
16.6%
(Missing)297
58.0%
ValueCountFrequency (%)
18
1.6%
35
 
1.0%
47
1.4%
54
 
0.8%
67
1.4%
76
 
1.2%
812
2.3%
142
 
0.4%
174
 
0.8%
1917
3.3%
ValueCountFrequency (%)
851
 
0.2%
8111
2.1%
8010
2.0%
772
 
0.4%
744
 
0.8%
729
1.8%
662
 
0.4%
652
 
0.4%
576
1.2%
565
1.0%

quantity
Real number (ℝ)

Missing 

Distinct25
Distinct (%)11.6%
Missing297
Missing (%)58.0%
Infinite0
Infinite (%)0.0%
Mean64.269767
Minimum0
Maximum300
Zeros2
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2025-10-30T18:23:06.468495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q120
median40
Q387
95-th percentile265
Maximum300
Range300
Interquartile range (IQR)67

Descriptive statistics

Standard deviation72.093214
Coefficient of variation (CV)1.1217283
Kurtosis4.0195746
Mean64.269767
Median Absolute Deviation (MAD)20
Skewness2.1260061
Sum13818
Variance5197.4316
MonotonicityNot monotonic
2025-10-30T18:23:06.772833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
40.043
 
8.4%
10.024
 
4.7%
100.022
 
4.3%
20.019
 
3.7%
50.017
 
3.3%
25.016
 
3.1%
30.016
 
3.1%
300.011
 
2.1%
200.08
 
1.6%
120.05
 
1.0%
Other values (15)34
 
6.6%
(Missing)297
58.0%
ValueCountFrequency (%)
0.02
 
0.4%
1.02
 
0.4%
3.02
 
0.4%
5.02
 
0.4%
10.024
4.7%
12.01
 
0.2%
15.04
 
0.8%
17.02
 
0.4%
20.019
3.7%
25.016
3.1%
ValueCountFrequency (%)
300.011
2.1%
250.02
 
0.4%
200.08
 
1.6%
125.02
 
0.4%
120.05
 
1.0%
110.01
 
0.2%
100.022
4.3%
90.02
 
0.4%
87.02
 
0.4%
80.04
 
0.8%

purchase_order_id
Real number (ℝ)

High correlation  Missing 

Distinct28
Distinct (%)40.0%
Missing442
Missing (%)86.3%
Infinite0
Infinite (%)0.0%
Mean102.1
Minimum90
Maximum148
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2025-10-30T18:23:06.900710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum90
5-th percentile90
Q192
median97
Q3105.75
95-th percentile144.2
Maximum148
Range58
Interquartile range (IQR)13.75

Descriptive statistics

Standard deviation15.55183
Coefficient of variation (CV)0.15231959
Kurtosis3.1476644
Mean102.1
Median Absolute Deviation (MAD)6
Skewness1.977922
Sum7147
Variance241.85942
MonotonicityNot monotonic
2025-10-30T18:23:07.018259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
9215
 
2.9%
917
 
1.4%
905
 
1.0%
933
 
0.6%
1042
 
0.4%
1002
 
0.4%
1112
 
0.4%
1012
 
0.4%
972
 
0.4%
1032
 
0.4%
Other values (18)28
 
5.5%
(Missing)442
86.3%
ValueCountFrequency (%)
905
 
1.0%
917
1.4%
9215
2.9%
933
 
0.6%
941
 
0.2%
951
 
0.2%
962
 
0.4%
972
 
0.4%
982
 
0.4%
992
 
0.4%
ValueCountFrequency (%)
1481
0.2%
1471
0.2%
1462
0.4%
1421
0.2%
1411
0.2%
1401
0.2%
1112
0.4%
1101
0.2%
1092
0.4%
1082
0.4%

customer_order_id
Unsupported

Missing  Rejected  Unsupported 

Missing512
Missing (%)100.0%
Memory size4.6 KiB

comments
Text

Missing 

Distinct12
Distinct (%)85.7%
Missing498
Missing (%)97.3%
Memory size17.0 KiB
2025-10-30T18:23:07.141582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters504
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)71.4%

Sample

1st rowFill Back Ordered product, Order #42
2nd rowFill Back Ordered product, Order #48
3rd rowFill Back Ordered product, Order #48
4th rowFill Back Ordered product, Order #33
5th rowFill Back Ordered product, Order #46
ValueCountFrequency (%)
fill14
16.7%
back14
16.7%
ordered14
16.7%
product14
16.7%
order14
16.7%
482
 
2.4%
462
 
2.4%
421
 
1.2%
331
 
1.2%
451
 
1.2%
Other values (7)7
8.3%
2025-10-30T18:23:07.360328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
70
13.9%
r70
13.9%
d56
 
11.1%
e42
 
8.3%
O28
 
5.6%
c28
 
5.6%
l28
 
5.6%
i14
 
2.8%
F14
 
2.8%
k14
 
2.8%
Other values (18)140
27.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)504
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
70
13.9%
r70
13.9%
d56
 
11.1%
e42
 
8.3%
O28
 
5.6%
c28
 
5.6%
l28
 
5.6%
i14
 
2.8%
F14
 
2.8%
k14
 
2.8%
Other values (18)140
27.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)504
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
70
13.9%
r70
13.9%
d56
 
11.1%
e42
 
8.3%
O28
 
5.6%
c28
 
5.6%
l28
 
5.6%
i14
 
2.8%
F14
 
2.8%
k14
 
2.8%
Other values (18)140
27.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)504
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
70
13.9%
r70
13.9%
d56
 
11.1%
e42
 
8.3%
O28
 
5.6%
c28
 
5.6%
l28
 
5.6%
i14
 
2.8%
F14
 
2.8%
k14
 
2.8%
Other values (18)140
27.8%

order_id
Real number (ℝ)

High correlation  Missing 

Distinct40
Distinct (%)43.0%
Missing419
Missing (%)81.8%
Infinite0
Infinite (%)0.0%
Mean52.946237
Minimum30
Maximum81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2025-10-30T18:23:07.466688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile31
Q139
median48
Q370
95-th percentile79
Maximum81
Range51
Interquartile range (IQR)31

Descriptive statistics

Standard deviation16.37294
Coefficient of variation (CV)0.30923709
Kurtosis-1.3167916
Mean52.946237
Median Absolute Deviation (MAD)13
Skewness0.29043732
Sum4924
Variance268.07317
MonotonicityNot monotonic
2025-10-30T18:23:07.576027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
514
 
0.8%
314
 
0.8%
424
 
0.8%
323
 
0.6%
303
 
0.6%
463
 
0.6%
793
 
0.6%
633
 
0.6%
583
 
0.6%
443
 
0.6%
Other values (30)60
 
11.7%
(Missing)419
81.8%
ValueCountFrequency (%)
303
0.6%
314
0.8%
323
0.6%
332
0.4%
342
0.4%
352
0.4%
362
0.4%
372
0.4%
382
0.4%
392
0.4%
ValueCountFrequency (%)
812
0.4%
801
 
0.2%
793
0.6%
782
0.4%
772
0.4%
762
0.4%
752
0.4%
742
0.4%
732
0.4%
722
0.4%

invoice_date
Date

Missing 

Distinct35
Distinct (%)100.0%
Missing477
Missing (%)93.2%
Memory size4.1 KiB
Minimum2006-03-22 16:08:59
Maximum2006-04-04 11:43:08
Invalid dates0
Invalid dates (%)0.0%
2025-10-30T18:23:07.690385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-30T18:23:07.843065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=35)

due_date
Unsupported

Missing  Rejected  Unsupported 

Missing512
Missing (%)100.0%
Memory size4.1 KiB

tax
Categorical

Constant  Missing 

Distinct1
Distinct (%)2.9%
Missing477
Missing (%)93.2%
Memory size28.3 KiB
0.0
35 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters105
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.035
 
6.8%
(Missing)477
93.2%

Length

2025-10-30T18:23:08.003080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-30T18:23:08.082151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.035
100.0%

Most occurring characters

ValueCountFrequency (%)
070
66.7%
.35
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)105
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
070
66.7%
.35
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)105
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
070
66.7%
.35
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)105
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
070
66.7%
.35
33.3%

shipping
Categorical

Constant  Missing 

Distinct1
Distinct (%)2.9%
Missing477
Missing (%)93.2%
Memory size28.3 KiB
0.0
35 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters105
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.035
 
6.8%
(Missing)477
93.2%

Length

2025-10-30T18:23:08.154098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-30T18:23:08.215554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.035
100.0%

Most occurring characters

ValueCountFrequency (%)
070
66.7%
.35
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)105
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
070
66.7%
.35
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)105
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
070
66.7%
.35
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)105
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
070
66.7%
.35
33.3%

amount_due
Categorical

Constant  Missing 

Distinct1
Distinct (%)2.9%
Missing477
Missing (%)93.2%
Memory size28.3 KiB
0.0
35 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters105
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.035
 
6.8%
(Missing)477
93.2%

Length

2025-10-30T18:23:08.286579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-30T18:23:08.344409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.035
100.0%

Most occurring characters

ValueCountFrequency (%)
070
66.7%
.35
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)105
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
070
66.7%
.35
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)105
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
070
66.7%
.35
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)105
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
070
66.7%
.35
33.3%

unit_price
Real number (ℝ)

High correlation  Missing 

Distinct22
Distinct (%)37.9%
Missing454
Missing (%)88.7%
Infinite0
Infinite (%)0.0%
Mean22.271724
Minimum2.99
Maximum81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2025-10-30T18:23:08.401463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2.99
5-th percentile2.99
Q19.65
median18
Q337.2
95-th percentile47.05
Maximum81
Range78.01
Interquartile range (IQR)27.55

Descriptive statistics

Standard deviation16.902057
Coefficient of variation (CV)0.75890204
Kurtosis0.98226676
Mean22.271724
Median Absolute Deviation (MAD)8.8
Skewness1.0515845
Sum1291.76
Variance285.67953
MonotonicityIncreasing
2025-10-30T18:23:08.523942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
465
 
1.0%
12.755
 
1.0%
9.24
 
0.8%
9.654
 
0.8%
2.994
 
0.8%
3.54
 
0.8%
404
 
0.8%
103
 
0.6%
18.43
 
0.6%
143
 
0.6%
Other values (12)19
 
3.7%
(Missing)454
88.7%
ValueCountFrequency (%)
2.994
0.8%
3.54
0.8%
71
 
0.2%
9.24
0.8%
9.654
0.8%
103
0.6%
12.755
1.0%
143
0.6%
182
 
0.4%
18.43
0.6%
ValueCountFrequency (%)
811
 
0.2%
532
 
0.4%
465
1.0%
404
0.8%
391
 
0.2%
382
 
0.4%
34.82
 
0.4%
302
 
0.4%
252
 
0.4%
222
 
0.4%

discount
Categorical

Constant  Missing 

Distinct1
Distinct (%)1.7%
Missing454
Missing (%)88.7%
Memory size28.4 KiB
0.0
58 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters174
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.058
 
11.3%
(Missing)454
88.7%

Length

2025-10-30T18:23:08.636322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-30T18:23:08.720741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.058
100.0%

Most occurring characters

ValueCountFrequency (%)
0116
66.7%
.58
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)174
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0116
66.7%
.58
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)174
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0116
66.7%
.58
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)174
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0116
66.7%
.58
33.3%

status_id
Categorical

High correlation  Missing 

Distinct5
Distinct (%)3.7%
Missing378
Missing (%)73.8%
Memory size28.4 KiB
2
75 
3
31 
0
17 
1
10 
5
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters134
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.7%

Sample

1st row2
2nd row1
3rd row1
4th row5
5th row2

Common Values

ValueCountFrequency (%)
275
 
14.6%
331
 
6.1%
017
 
3.3%
110
 
2.0%
51
 
0.2%
(Missing)378
73.8%

Length

2025-10-30T18:23:08.816181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-30T18:23:08.922015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
275
56.0%
331
23.1%
017
 
12.7%
110
 
7.5%
51
 
0.7%

Most occurring characters

ValueCountFrequency (%)
275
56.0%
331
23.1%
017
 
12.7%
110
 
7.5%
51
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)134
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
275
56.0%
331
23.1%
017
 
12.7%
110
 
7.5%
51
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)134
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
275
56.0%
331
23.1%
017
 
12.7%
110
 
7.5%
51
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)134
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
275
56.0%
331
23.1%
017
 
12.7%
110
 
7.5%
51
 
0.7%

date_allocated
Unsupported

Missing  Rejected  Unsupported 

Missing512
Missing (%)100.0%
Memory size4.1 KiB

inventory_id
Real number (ℝ)

High correlation  Missing 

Distinct99
Distinct (%)100.0%
Missing413
Missing (%)80.7%
Infinite0
Infinite (%)0.0%
Mean85.151515
Minimum35
Maximum136
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2025-10-30T18:23:09.061008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum35
5-th percentile39.9
Q159.5
median84
Q3111.5
95-th percentile131.1
Maximum136
Range101
Interquartile range (IQR)52

Descriptive statistics

Standard deviation29.968474
Coefficient of variation (CV)0.35194294
Kurtosis-1.238445
Mean85.151515
Median Absolute Deviation (MAD)26
Skewness0.033813886
Sum8430
Variance898.10946
MonotonicityNot monotonic
2025-10-30T18:23:09.223746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
731
 
0.2%
901
 
0.2%
871
 
0.2%
661
 
0.2%
861
 
0.2%
1291
 
0.2%
1351
 
0.2%
811
 
0.2%
691
 
0.2%
1101
 
0.2%
Other values (89)89
 
17.4%
(Missing)413
80.7%
ValueCountFrequency (%)
351
0.2%
361
0.2%
371
0.2%
381
0.2%
391
0.2%
401
0.2%
411
0.2%
421
0.2%
431
0.2%
441
0.2%
ValueCountFrequency (%)
1361
0.2%
1351
0.2%
1341
0.2%
1331
0.2%
1321
0.2%
1311
0.2%
1301
0.2%
1291
0.2%
1281
0.2%
1271
0.2%

status
Text

Missing 

Distinct10
Distinct (%)100.0%
Missing502
Missing (%)98.0%
Memory size16.4 KiB
2025-10-30T18:23:09.376947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length9
Median length8
Mean length7
Min length3

Characters and Unicode

Total characters70
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)100.0%

Sample

1st rowNone
2nd rowAllocated
3rd rowInvoiced
4th rowShipped
5th rowOn Order
ValueCountFrequency (%)
none1
8.3%
allocated1
8.3%
invoiced1
8.3%
shipped1
8.3%
on1
8.3%
order1
8.3%
no1
8.3%
stock1
8.3%
new1
8.3%
submitted1
8.3%
Other values (2)2
16.7%
2025-10-30T18:23:09.639130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e9
 
12.9%
o7
 
10.0%
d7
 
10.0%
t4
 
5.7%
p4
 
5.7%
N3
 
4.3%
c3
 
4.3%
l3
 
4.3%
S3
 
4.3%
n3
 
4.3%
Other values (16)24
34.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)70
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e9
 
12.9%
o7
 
10.0%
d7
 
10.0%
t4
 
5.7%
p4
 
5.7%
N3
 
4.3%
c3
 
4.3%
l3
 
4.3%
S3
 
4.3%
n3
 
4.3%
Other values (16)24
34.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)70
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e9
 
12.9%
o7
 
10.0%
d7
 
10.0%
t4
 
5.7%
p4
 
5.7%
N3
 
4.3%
c3
 
4.3%
l3
 
4.3%
S3
 
4.3%
n3
 
4.3%
Other values (16)24
34.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)70
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e9
 
12.9%
o7
 
10.0%
d7
 
10.0%
t4
 
5.7%
p4
 
5.7%
N3
 
4.3%
c3
 
4.3%
l3
 
4.3%
S3
 
4.3%
n3
 
4.3%
Other values (16)24
34.3%

customer_id
Real number (ℝ)

High correlation  Missing 

Distinct15
Distinct (%)31.2%
Missing464
Missing (%)90.6%
Infinite0
Infinite (%)0.0%
Mean12.854167
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2025-10-30T18:23:09.732608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q16
median8.5
Q325.25
95-th percentile29
Maximum29
Range28
Interquartile range (IQR)19.25

Descriptive statistics

Standard deviation9.8152933
Coefficient of variation (CV)0.76358846
Kurtosis-1.1598277
Mean12.854167
Median Absolute Deviation (MAD)4
Skewness0.74212086
Sum617
Variance96.339982
MonotonicityNot monotonic
2025-10-30T18:23:09.832344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
86
 
1.2%
66
 
1.2%
45
 
1.0%
104
 
0.8%
294
 
0.8%
284
 
0.8%
33
 
0.6%
252
 
0.4%
12
 
0.4%
272
 
0.4%
Other values (5)10
 
2.0%
(Missing)464
90.6%
ValueCountFrequency (%)
12
 
0.4%
33
0.6%
45
1.0%
66
1.2%
72
 
0.4%
86
1.2%
92
 
0.4%
104
0.8%
112
 
0.4%
122
 
0.4%
ValueCountFrequency (%)
294
0.8%
284
0.8%
272
 
0.4%
262
 
0.4%
252
 
0.4%
122
 
0.4%
112
 
0.4%
104
0.8%
92
 
0.4%
86
1.2%

order_date
Date

Missing 

Distinct28
Distinct (%)58.3%
Missing464
Missing (%)90.6%
Memory size4.1 KiB
Minimum2006-01-15 00:00:00
Maximum2006-06-23 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-10-30T18:23:09.950630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-30T18:23:10.081930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=28)

shipped_date
Date

Missing 

Distinct23
Distinct (%)59.0%
Missing473
Missing (%)92.4%
Memory size4.1 KiB
Minimum2006-01-22 00:00:00
Maximum2006-06-23 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-10-30T18:23:10.199688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-30T18:23:10.543169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=23)

shipper_id
Categorical

High correlation  Missing 

Distinct3
Distinct (%)7.0%
Missing469
Missing (%)91.6%
Memory size28.2 KiB
2
18 
3
17 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters43
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row3
5th row3

Common Values

ValueCountFrequency (%)
218
 
3.5%
317
 
3.3%
18
 
1.6%
(Missing)469
91.6%

Length

2025-10-30T18:23:10.680380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-30T18:23:10.733577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
218
41.9%
317
39.5%
18
18.6%

Most occurring characters

ValueCountFrequency (%)
218
41.9%
317
39.5%
18
18.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)43
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
218
41.9%
317
39.5%
18
18.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)43
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
218
41.9%
317
39.5%
18
18.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)43
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
218
41.9%
317
39.5%
18
18.6%

ship_name
Categorical

High correlation  Missing 

Distinct15
Distinct (%)31.2%
Missing464
Missing (%)90.6%
Memory size29.1 KiB
Elizabeth Andersen
Francisco Pérez-Olaeta
Christina Lee
Amritansh Raghav
Roland Wacker
Other values (10)
23 

Length

Max length22
Median length16
Mean length14.208333
Min length7

Characters and Unicode

Total characters682
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJohn Edwards
2nd rowKaren Toh
3rd rowChristina Lee
4th rowChristina Lee
5th rowElizabeth Andersen

Common Values

ValueCountFrequency (%)
Elizabeth Andersen6
 
1.2%
Francisco Pérez-Olaeta6
 
1.2%
Christina Lee5
 
1.0%
Amritansh Raghav4
 
0.8%
Roland Wacker4
 
0.8%
Soo Jung Lee4
 
0.8%
Thomas Axen3
 
0.6%
Anna Bedecs2
 
0.4%
Karen Toh2
 
0.4%
John Edwards2
 
0.4%
Other values (5)10
 
2.0%
(Missing)464
90.6%

Length

2025-10-30T18:23:10.872788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
lee9
 
9.0%
andersen6
 
6.0%
francisco6
 
6.0%
pérez-olaeta6
 
6.0%
elizabeth6
 
6.0%
christina5
 
5.0%
amritansh4
 
4.0%
raghav4
 
4.0%
roland4
 
4.0%
wacker4
 
4.0%
Other values (19)46
46.0%

Most occurring characters

ValueCountFrequency (%)
e75
 
11.0%
n64
 
9.4%
a62
 
9.1%
52
 
7.6%
r41
 
6.0%
s32
 
4.7%
i32
 
4.7%
o31
 
4.5%
h30
 
4.4%
t25
 
3.7%
Other values (31)238
34.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)682
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e75
 
11.0%
n64
 
9.4%
a62
 
9.1%
52
 
7.6%
r41
 
6.0%
s32
 
4.7%
i32
 
4.7%
o31
 
4.5%
h30
 
4.4%
t25
 
3.7%
Other values (31)238
34.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)682
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e75
 
11.0%
n64
 
9.4%
a62
 
9.1%
52
 
7.6%
r41
 
6.0%
s32
 
4.7%
i32
 
4.7%
o31
 
4.5%
h30
 
4.4%
t25
 
3.7%
Other values (31)238
34.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)682
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e75
 
11.0%
n64
 
9.4%
a62
 
9.1%
52
 
7.6%
r41
 
6.0%
s32
 
4.7%
i32
 
4.7%
o31
 
4.5%
h30
 
4.4%
t25
 
3.7%
Other values (31)238
34.9%

ship_address
Categorical

High correlation  Missing 

Distinct15
Distinct (%)31.2%
Missing464
Missing (%)90.6%
Memory size28.8 KiB
123 8th Street
123 6th Street
123 4th Street
789 28th Street
123 10th Street
Other values (10)
23 

Length

Max length15
Median length14
Mean length14.458333
Min length14

Characters and Unicode

Total characters694
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row123 12th Street
2nd row789 27th Street
3rd row123 4th Street
4th row123 4th Street
5th row123 8th Street

Common Values

ValueCountFrequency (%)
123 8th Street6
 
1.2%
123 6th Street6
 
1.2%
123 4th Street5
 
1.0%
789 28th Street4
 
0.8%
123 10th Street4
 
0.8%
789 29th Street4
 
0.8%
123 3rd Street3
 
0.6%
123 1st Street2
 
0.4%
789 27th Street2
 
0.4%
123 12th Street2
 
0.4%
Other values (5)10
 
2.0%
(Missing)464
90.6%

Length

2025-10-30T18:23:10.999654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
street48
33.3%
12334
23.6%
78914
 
9.7%
8th6
 
4.2%
6th6
 
4.2%
4th5
 
3.5%
28th4
 
2.8%
10th4
 
2.8%
29th4
 
2.8%
3rd3
 
2.1%
Other values (8)16
 
11.1%

Most occurring characters

ValueCountFrequency (%)
t141
20.3%
96
13.8%
e96
13.8%
r51
 
7.3%
250
 
7.2%
S48
 
6.9%
146
 
6.6%
h43
 
6.2%
337
 
5.3%
824
 
3.5%
Other values (8)62
8.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)694
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t141
20.3%
96
13.8%
e96
13.8%
r51
 
7.3%
250
 
7.2%
S48
 
6.9%
146
 
6.6%
h43
 
6.2%
337
 
5.3%
824
 
3.5%
Other values (8)62
8.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)694
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t141
20.3%
96
13.8%
e96
13.8%
r51
 
7.3%
250
 
7.2%
S48
 
6.9%
146
 
6.6%
h43
 
6.2%
337
 
5.3%
824
 
3.5%
Other values (8)62
8.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)694
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t141
20.3%
96
13.8%
e96
13.8%
r51
 
7.3%
250
 
7.2%
S48
 
6.9%
146
 
6.6%
h43
 
6.2%
337
 
5.3%
824
 
3.5%
Other values (8)62
8.9%

ship_city
Categorical

High correlation  Missing 

Distinct12
Distinct (%)25.0%
Missing464
Missing (%)90.6%
Memory size28.5 KiB
Portland
Milwaukee
Chicago
New York
Memphis
Other values (7)
21 

Length

Max length14
Median length9
Mean length7.8541667
Min length5

Characters and Unicode

Total characters377
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLas Vegas
2nd rowLas Vegas
3rd rowNew York
4th rowNew York
5th rowPortland

Common Values

ValueCountFrequency (%)
Portland6
 
1.2%
Milwaukee6
 
1.2%
Chicago6
 
1.2%
New York5
 
1.0%
Memphis4
 
0.8%
Las Vegas4
 
0.8%
Denver4
 
0.8%
Miami4
 
0.8%
Los Angelas3
 
0.6%
Seattle2
 
0.4%
Other values (2)4
 
0.8%
(Missing)464
90.6%

Length

2025-10-30T18:23:11.118804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
portland6
 
9.4%
milwaukee6
 
9.4%
chicago6
 
9.4%
new5
 
7.8%
york5
 
7.8%
memphis4
 
6.2%
las4
 
6.2%
vegas4
 
6.2%
denver4
 
6.2%
miami4
 
6.2%
Other values (7)16
25.0%

Most occurring characters

ValueCountFrequency (%)
e44
 
11.7%
a39
 
10.3%
i28
 
7.4%
o22
 
5.8%
s20
 
5.3%
l19
 
5.0%
16
 
4.2%
r15
 
4.0%
t14
 
3.7%
M14
 
3.7%
Other values (22)146
38.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)377
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e44
 
11.7%
a39
 
10.3%
i28
 
7.4%
o22
 
5.8%
s20
 
5.3%
l19
 
5.0%
16
 
4.2%
r15
 
4.0%
t14
 
3.7%
M14
 
3.7%
Other values (22)146
38.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)377
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e44
 
11.7%
a39
 
10.3%
i28
 
7.4%
o22
 
5.8%
s20
 
5.3%
l19
 
5.0%
16
 
4.2%
r15
 
4.0%
t14
 
3.7%
M14
 
3.7%
Other values (22)146
38.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)377
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e44
 
11.7%
a39
 
10.3%
i28
 
7.4%
o22
 
5.8%
s20
 
5.3%
l19
 
5.0%
16
 
4.2%
r15
 
4.0%
t14
 
3.7%
M14
 
3.7%
Other values (22)146
38.7%

ship_state_province
Categorical

High correlation  Missing 

Distinct12
Distinct (%)25.0%
Missing464
Missing (%)90.6%
Memory size28.3 KiB
OR
WI
IL
NY
TN
Other values (7)
21 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters96
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNV
2nd rowNV
3rd rowNY
4th rowNY
5th rowOR

Common Values

ValueCountFrequency (%)
OR6
 
1.2%
WI6
 
1.2%
IL6
 
1.2%
NY5
 
1.0%
TN4
 
0.8%
NV4
 
0.8%
CO4
 
0.8%
FL4
 
0.8%
CA3
 
0.6%
WA2
 
0.4%
Other values (2)4
 
0.8%
(Missing)464
90.6%

Length

2025-10-30T18:23:11.232145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
or6
12.5%
wi6
12.5%
il6
12.5%
ny5
10.4%
tn4
8.3%
nv4
8.3%
co4
8.3%
fl4
8.3%
ca3
6.2%
wa2
 
4.2%
Other values (2)4
8.3%

Most occurring characters

ValueCountFrequency (%)
I14
14.6%
N13
13.5%
L10
10.4%
O10
10.4%
W8
8.3%
C7
7.3%
R6
6.2%
T6
6.2%
Y5
 
5.2%
A5
 
5.2%
Other values (4)12
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)96
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I14
14.6%
N13
13.5%
L10
10.4%
O10
10.4%
W8
8.3%
C7
7.3%
R6
6.2%
T6
6.2%
Y5
 
5.2%
A5
 
5.2%
Other values (4)12
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)96
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I14
14.6%
N13
13.5%
L10
10.4%
O10
10.4%
W8
8.3%
C7
7.3%
R6
6.2%
T6
6.2%
Y5
 
5.2%
A5
 
5.2%
Other values (4)12
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)96
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I14
14.6%
N13
13.5%
L10
10.4%
O10
10.4%
W8
8.3%
C7
7.3%
R6
6.2%
T6
6.2%
Y5
 
5.2%
A5
 
5.2%
Other values (4)12
12.5%

ship_zip_postal_code
Categorical

Constant  Missing 

Distinct1
Distinct (%)2.1%
Missing464
Missing (%)90.6%
Memory size28.4 KiB
99999
48 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters240
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row99999
2nd row99999
3rd row99999
4th row99999
5th row99999

Common Values

ValueCountFrequency (%)
9999948
 
9.4%
(Missing)464
90.6%

Length

2025-10-30T18:23:11.309924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-30T18:23:11.357607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
9999948
100.0%

Most occurring characters

ValueCountFrequency (%)
9240
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)240
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9240
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)240
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9240
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)240
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9240
100.0%

ship_country_region
Categorical

Constant  Missing 

Distinct1
Distinct (%)2.1%
Missing464
Missing (%)90.6%
Memory size28.3 KiB
USA
48 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters144
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUSA
2nd rowUSA
3rd rowUSA
4th rowUSA
5th rowUSA

Common Values

ValueCountFrequency (%)
USA48
 
9.4%
(Missing)464
90.6%

Length

2025-10-30T18:23:11.459643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-30T18:23:11.538386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
usa48
100.0%

Most occurring characters

ValueCountFrequency (%)
U48
33.3%
S48
33.3%
A48
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)144
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U48
33.3%
S48
33.3%
A48
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)144
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U48
33.3%
S48
33.3%
A48
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)144
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U48
33.3%
S48
33.3%
A48
33.3%

shipping_fee
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct13
Distinct (%)17.1%
Missing436
Missing (%)85.2%
Infinite0
Infinite (%)0.0%
Mean28.131579
Minimum0
Maximum300
Zeros40
Zeros (%)7.8%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2025-10-30T18:23:11.606112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q310
95-th percentile200
Maximum300
Range300
Interquartile range (IQR)10

Descriptive statistics

Standard deviation65.455602
Coefficient of variation (CV)2.326766
Kurtosis8.2873457
Mean28.131579
Median Absolute Deviation (MAD)0
Skewness2.9423851
Sum2138
Variance4284.4358
MonotonicityNot monotonic
2025-10-30T18:23:11.701050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
040
 
7.8%
58
 
1.6%
2004
 
0.8%
74
 
0.8%
504
 
0.8%
402
 
0.4%
42
 
0.4%
122
 
0.4%
102
 
0.4%
3002
 
0.4%
Other values (3)6
 
1.2%
(Missing)436
85.2%
ValueCountFrequency (%)
040
7.8%
42
 
0.4%
58
 
1.6%
74
 
0.8%
92
 
0.4%
102
 
0.4%
122
 
0.4%
402
 
0.4%
504
 
0.8%
602
 
0.4%
ValueCountFrequency (%)
3002
0.4%
2004
0.8%
1002
0.4%
602
0.4%
504
0.8%
402
0.4%
122
0.4%
102
0.4%
92
0.4%
74
0.8%

taxes
Categorical

Constant  Missing 

Distinct1
Distinct (%)1.3%
Missing436
Missing (%)85.2%
Memory size28.4 KiB
0.0
76 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters228
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.076
 
14.8%
(Missing)436
85.2%

Length

2025-10-30T18:23:11.787760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-30T18:23:11.837255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.076
100.0%

Most occurring characters

ValueCountFrequency (%)
0152
66.7%
.76
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)228
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0152
66.7%
.76
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)228
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0152
66.7%
.76
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)228
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0152
66.7%
.76
33.3%

payment_type
Categorical

High correlation  Missing 

Distinct3
Distinct (%)7.9%
Missing474
Missing (%)92.6%
Memory size28.4 KiB
Check
18 
Credit Card
16 
Cash

Length

Max length11
Median length5
Mean length7.4210526
Min length4

Characters and Unicode

Total characters282
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCredit Card
2nd rowCheck
3rd rowCredit Card
4th rowCheck
5th rowCredit Card

Common Values

ValueCountFrequency (%)
Check18
 
3.5%
Credit Card16
 
3.1%
Cash4
 
0.8%
(Missing)474
92.6%

Length

2025-10-30T18:23:11.904894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-30T18:23:11.962892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
check18
33.3%
credit16
29.6%
card16
29.6%
cash4
 
7.4%

Most occurring characters

ValueCountFrequency (%)
C54
19.1%
e34
12.1%
d32
11.3%
r32
11.3%
h22
7.8%
a20
 
7.1%
c18
 
6.4%
k18
 
6.4%
i16
 
5.7%
t16
 
5.7%
Other values (2)20
 
7.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)282
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C54
19.1%
e34
12.1%
d32
11.3%
r32
11.3%
h22
7.8%
a20
 
7.1%
c18
 
6.4%
k18
 
6.4%
i16
 
5.7%
t16
 
5.7%
Other values (2)20
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)282
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C54
19.1%
e34
12.1%
d32
11.3%
r32
11.3%
h22
7.8%
a20
 
7.1%
c18
 
6.4%
k18
 
6.4%
i16
 
5.7%
t16
 
5.7%
Other values (2)20
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)282
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C54
19.1%
e34
12.1%
d32
11.3%
r32
11.3%
h22
7.8%
a20
 
7.1%
c18
 
6.4%
k18
 
6.4%
i16
 
5.7%
t16
 
5.7%
Other values (2)20
 
7.1%

paid_date
Date

Missing 

Distinct26
Distinct (%)68.4%
Missing474
Missing (%)92.6%
Memory size4.1 KiB
Minimum2006-01-15 00:00:00
Maximum2006-06-23 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-10-30T18:23:12.061940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-30T18:23:12.198325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=26)

tax_rate
Categorical

Constant  Missing 

Distinct1
Distinct (%)2.1%
Missing464
Missing (%)90.6%
Memory size28.3 KiB
0.0
48 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters144
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.048
 
9.4%
(Missing)464
90.6%

Length

2025-10-30T18:23:12.350082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-30T18:23:12.402478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.048
100.0%

Most occurring characters

ValueCountFrequency (%)
096
66.7%
.48
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)144
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
096
66.7%
.48
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)144
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
096
66.7%
.48
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)144
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
096
66.7%
.48
33.3%

tax_status_id
Unsupported

Missing  Rejected  Unsupported 

Missing512
Missing (%)100.0%
Memory size4.6 KiB

status_name
Text

Missing 

Distinct4
Distinct (%)100.0%
Missing508
Missing (%)99.2%
Memory size16.2 KiB
2025-10-30T18:23:12.496635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length8
Median length6.5
Mean length6
Min length3

Characters and Unicode

Total characters24
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)100.0%

Sample

1st rowNew
2nd rowInvoiced
3rd rowShipped
4th rowClosed
ValueCountFrequency (%)
new1
25.0%
invoiced1
25.0%
shipped1
25.0%
closed1
25.0%
2025-10-30T18:23:12.694526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e4
16.7%
d3
12.5%
o2
 
8.3%
i2
 
8.3%
p2
 
8.3%
w1
 
4.2%
I1
 
4.2%
N1
 
4.2%
n1
 
4.2%
v1
 
4.2%
Other values (6)6
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)24
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e4
16.7%
d3
12.5%
o2
 
8.3%
i2
 
8.3%
p2
 
8.3%
w1
 
4.2%
I1
 
4.2%
N1
 
4.2%
n1
 
4.2%
v1
 
4.2%
Other values (6)6
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)24
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e4
16.7%
d3
12.5%
o2
 
8.3%
i2
 
8.3%
p2
 
8.3%
w1
 
4.2%
I1
 
4.2%
N1
 
4.2%
n1
 
4.2%
v1
 
4.2%
Other values (6)6
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)24
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e4
16.7%
d3
12.5%
o2
 
8.3%
i2
 
8.3%
p2
 
8.3%
w1
 
4.2%
I1
 
4.2%
N1
 
4.2%
n1
 
4.2%
v1
 
4.2%
Other values (6)6
25.0%

tax_status_name
Text

Missing 

Distinct2
Distinct (%)100.0%
Missing510
Missing (%)99.6%
Memory size16.2 KiB
2025-10-30T18:23:12.781484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length8.5
Mean length8.5
Min length7

Characters and Unicode

Total characters17
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st rowTax Exempt
2nd rowTaxable
ValueCountFrequency (%)
tax1
33.3%
exempt1
33.3%
taxable1
33.3%
2025-10-30T18:23:13.039983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a3
17.6%
x3
17.6%
T2
11.8%
e2
11.8%
1
 
5.9%
E1
 
5.9%
m1
 
5.9%
p1
 
5.9%
t1
 
5.9%
b1
 
5.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)17
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a3
17.6%
x3
17.6%
T2
11.8%
e2
11.8%
1
 
5.9%
E1
 
5.9%
m1
 
5.9%
p1
 
5.9%
t1
 
5.9%
b1
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)17
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a3
17.6%
x3
17.6%
T2
11.8%
e2
11.8%
1
 
5.9%
E1
 
5.9%
m1
 
5.9%
p1
 
5.9%
t1
 
5.9%
b1
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)17
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a3
17.6%
x3
17.6%
T2
11.8%
e2
11.8%
1
 
5.9%
E1
 
5.9%
m1
 
5.9%
p1
 
5.9%
t1
 
5.9%
b1
 
5.9%

privilege_name
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing511
Missing (%)99.8%
Memory size16.2 KiB
2025-10-30T18:23:13.140795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length18
Median length18
Mean length18
Min length18

Characters and Unicode

Total characters18
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowPurchase Approvals
ValueCountFrequency (%)
purchase1
50.0%
approvals1
50.0%
2025-10-30T18:23:13.369160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a2
11.1%
r2
11.1%
p2
11.1%
s2
11.1%
u1
 
5.6%
P1
 
5.6%
h1
 
5.6%
c1
 
5.6%
1
 
5.6%
e1
 
5.6%
Other values (4)4
22.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)18
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a2
11.1%
r2
11.1%
p2
11.1%
s2
11.1%
u1
 
5.6%
P1
 
5.6%
h1
 
5.6%
c1
 
5.6%
1
 
5.6%
e1
 
5.6%
Other values (4)4
22.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)18
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a2
11.1%
r2
11.1%
p2
11.1%
s2
11.1%
u1
 
5.6%
P1
 
5.6%
h1
 
5.6%
c1
 
5.6%
1
 
5.6%
e1
 
5.6%
Other values (4)4
22.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)18
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a2
11.1%
r2
11.1%
p2
11.1%
s2
11.1%
u1
 
5.6%
P1
 
5.6%
h1
 
5.6%
c1
 
5.6%
1
 
5.6%
e1
 
5.6%
Other values (4)4
22.2%

supplier_ids
Categorical

High correlation  Missing 

Distinct12
Distinct (%)26.7%
Missing467
Missing (%)91.2%
Memory size28.2 KiB
6
11 
1
10
7
2;6
Other values (7)
12 

Length

Max length3
Median length1
Mean length1.3333333
Min length1

Characters and Unicode

Total characters60
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)8.9%

Sample

1st row6
2nd row6
3rd row6
4th row6
5th row6

Common Values

ValueCountFrequency (%)
611
 
2.1%
19
 
1.8%
105
 
1.0%
74
 
0.8%
2;64
 
0.8%
23
 
0.6%
83
 
0.6%
42
 
0.4%
91
 
0.2%
51
 
0.2%
Other values (2)2
 
0.4%
(Missing)467
91.2%

Length

2025-10-30T18:23:13.474556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
611
24.4%
19
20.0%
105
11.1%
74
 
8.9%
2;64
 
8.9%
23
 
6.7%
83
 
6.7%
42
 
4.4%
91
 
2.2%
51
 
2.2%
Other values (2)2
 
4.4%

Most occurring characters

ValueCountFrequency (%)
615
25.0%
114
23.3%
27
11.7%
05
 
8.3%
;5
 
8.3%
74
 
6.7%
83
 
5.0%
43
 
5.0%
32
 
3.3%
91
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)60
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
615
25.0%
114
23.3%
27
11.7%
05
 
8.3%
;5
 
8.3%
74
 
6.7%
83
 
5.0%
43
 
5.0%
32
 
3.3%
91
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)60
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
615
25.0%
114
23.3%
27
11.7%
05
 
8.3%
;5
 
8.3%
74
 
6.7%
83
 
5.0%
43
 
5.0%
32
 
3.3%
91
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)60
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
615
25.0%
114
23.3%
27
11.7%
05
 
8.3%
;5
 
8.3%
74
 
6.7%
83
 
5.0%
43
 
5.0%
32
 
3.3%
91
 
1.7%

product_code
Text

Missing 

Distinct43
Distinct (%)95.6%
Missing467
Missing (%)91.2%
Memory size17.6 KiB
2025-10-30T18:23:13.648995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length9
Median length8
Mean length7.8666667
Min length6

Characters and Unicode

Total characters354
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41 ?
Unique (%)91.1%

Sample

1st rowNWTCFV-88
2nd rowNWTCFV-89
3rd rowNWTCFV-93
4th rowNWTCFV-94
5th rowNWTCFV-92
ValueCountFrequency (%)
nwtc-822
 
4.4%
nwtjp-62
 
4.4%
nwtcfv-891
 
2.2%
nwtcfv-931
 
2.2%
nwtcfv-941
 
2.2%
nwtcfv-911
 
2.2%
nwtcfv-921
 
2.2%
nwtcfv-901
 
2.2%
nwtb-871
 
2.2%
nwtcm-961
 
2.2%
Other values (33)33
73.3%
2025-10-30T18:23:13.942578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N50
14.1%
W45
12.7%
T45
12.7%
-45
12.7%
C18
 
5.1%
814
 
4.0%
F13
 
3.7%
912
 
3.4%
49
 
2.5%
19
 
2.5%
Other values (16)94
26.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)354
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N50
14.1%
W45
12.7%
T45
12.7%
-45
12.7%
C18
 
5.1%
814
 
4.0%
F13
 
3.7%
912
 
3.4%
49
 
2.5%
19
 
2.5%
Other values (16)94
26.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)354
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N50
14.1%
W45
12.7%
T45
12.7%
-45
12.7%
C18
 
5.1%
814
 
4.0%
F13
 
3.7%
912
 
3.4%
49
 
2.5%
19
 
2.5%
Other values (16)94
26.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)354
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N50
14.1%
W45
12.7%
T45
12.7%
-45
12.7%
C18
 
5.1%
814
 
4.0%
F13
 
3.7%
912
 
3.4%
49
 
2.5%
19
 
2.5%
Other values (16)94
26.6%

product_name
Text

Missing 

Distinct45
Distinct (%)100.0%
Missing467
Missing (%)91.2%
Memory size18.4 KiB
2025-10-30T18:23:14.121745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length40
Median length33
Mean length27.8
Min length21

Characters and Unicode

Total characters1251
Distinct characters43
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique45 ?
Unique (%)100.0%

Sample

1st rowNorthwind Traders Pears
2nd rowNorthwind Traders Peaches
3rd rowNorthwind Traders Corn
4th rowNorthwind Traders Peas
5th rowNorthwind Traders Green Beans
ValueCountFrequency (%)
northwind45
27.4%
traders45
27.4%
mix3
 
1.8%
dried3
 
1.8%
sauce3
 
1.8%
chocolate2
 
1.2%
tea2
 
1.2%
pears2
 
1.2%
hot2
 
1.2%
green2
 
1.2%
Other values (54)55
33.5%
2025-10-30T18:23:14.429600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r163
13.0%
119
 
9.5%
d99
 
7.9%
e96
 
7.7%
a84
 
6.7%
o73
 
5.8%
i72
 
5.8%
n66
 
5.3%
s62
 
5.0%
t59
 
4.7%
Other values (33)358
28.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)1251
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r163
13.0%
119
 
9.5%
d99
 
7.9%
e96
 
7.7%
a84
 
6.7%
o73
 
5.8%
i72
 
5.8%
n66
 
5.3%
s62
 
5.0%
t59
 
4.7%
Other values (33)358
28.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1251
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r163
13.0%
119
 
9.5%
d99
 
7.9%
e96
 
7.7%
a84
 
6.7%
o73
 
5.8%
i72
 
5.8%
n66
 
5.3%
s62
 
5.0%
t59
 
4.7%
Other values (33)358
28.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1251
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r163
13.0%
119
 
9.5%
d99
 
7.9%
e96
 
7.7%
a84
 
6.7%
o73
 
5.8%
i72
 
5.8%
n66
 
5.3%
s62
 
5.0%
t59
 
4.7%
Other values (33)358
28.6%

description
Unsupported

Missing  Rejected  Unsupported 

Missing512
Missing (%)100.0%
Memory size4.1 KiB

standard_cost
Real number (ℝ)

High correlation  Missing 

Distinct29
Distinct (%)64.4%
Missing467
Missing (%)91.2%
Infinite0
Infinite (%)0.0%
Mean11.6825
Minimum0.5
Maximum60.75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2025-10-30T18:23:14.548353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile1
Q12
median7.5
Q316.0125
95-th percentile33.6
Maximum60.75
Range60.25
Interquartile range (IQR)14.0125

Descriptive statistics

Standard deviation12.689461
Coefficient of variation (CV)1.086194
Kurtosis4.0544357
Mean11.6825
Median Absolute Deviation (MAD)6.5
Skewness1.7941241
Sum525.7125
Variance161.02242
MonotonicityNot monotonic
2025-10-30T18:23:14.689268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
19
 
1.8%
24
 
0.8%
7.53
 
0.6%
32
 
0.4%
0.52
 
0.4%
10.52
 
0.4%
6.91
 
0.2%
91
 
0.2%
16.01251
 
0.2%
29.251
 
0.2%
Other values (19)19
 
3.7%
(Missing)467
91.2%
ValueCountFrequency (%)
0.52
 
0.4%
19
1.8%
24
0.8%
32
 
0.4%
5.251
 
0.2%
6.91
 
0.2%
7.23751
 
0.2%
7.53
 
0.6%
91
 
0.2%
9.56251
 
0.2%
ValueCountFrequency (%)
60.751
0.2%
39.751
0.2%
34.51
0.2%
301
0.2%
29.251
0.2%
28.51
0.2%
26.11
0.2%
22.51
0.2%
18.751
0.2%
17.43751
0.2%

list_price
Real number (ℝ)

High correlation  Missing 

Distinct37
Distinct (%)82.2%
Missing467
Missing (%)91.2%
Infinite0
Infinite (%)0.0%
Mean15.845778
Minimum1.2
Maximum81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2025-10-30T18:23:14.804103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.2
5-th percentile1.34
Q12.99
median10
Q321.35
95-th percentile44.8
Maximum81
Range79.8
Interquartile range (IQR)18.36

Descriptive statistics

Standard deviation16.743022
Coefficient of variation (CV)1.0566236
Kurtosis4.2057324
Mean15.845778
Median Absolute Deviation (MAD)8.11
Skewness1.8263175
Sum713.06
Variance280.3288
MonotonicityNot monotonic
2025-10-30T18:23:14.972847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
43
 
0.6%
103
 
0.6%
1.22
 
0.4%
22
 
0.4%
1.52
 
0.4%
1.82
 
0.4%
1.31
 
0.2%
1.891
 
0.2%
51
 
0.2%
1.951
 
0.2%
Other values (27)27
 
5.3%
(Missing)467
91.2%
ValueCountFrequency (%)
1.22
0.4%
1.31
 
0.2%
1.52
0.4%
1.82
0.4%
1.891
 
0.2%
1.951
 
0.2%
22
0.4%
2.991
 
0.2%
3.51
 
0.2%
43
0.6%
ValueCountFrequency (%)
811
0.2%
531
0.2%
461
0.2%
401
0.2%
391
0.2%
381
0.2%
34.81
0.2%
301
0.2%
251
0.2%
23.251
0.2%

reorder_level
Real number (ℝ)

High correlation  Missing 

Distinct8
Distinct (%)17.8%
Missing467
Missing (%)91.2%
Infinite0
Infinite (%)0.0%
Mean22.444444
Minimum5
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2025-10-30T18:23:15.090655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile6
Q110
median10
Q325
95-th percentile90
Maximum100
Range95
Interquartile range (IQR)15

Descriptive statistics

Standard deviation23.442924
Coefficient of variation (CV)1.0444867
Kurtosis6.3139457
Mean22.444444
Median Absolute Deviation (MAD)5
Skewness2.5591877
Sum1010
Variance549.57071
MonotonicityNot monotonic
2025-10-30T18:23:15.401425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1021
 
4.1%
305
 
1.0%
255
 
1.0%
204
 
0.8%
53
 
0.6%
1003
 
0.6%
502
 
0.4%
152
 
0.4%
(Missing)467
91.2%
ValueCountFrequency (%)
53
 
0.6%
1021
4.1%
152
 
0.4%
204
 
0.8%
255
 
1.0%
305
 
1.0%
502
 
0.4%
1003
 
0.6%
ValueCountFrequency (%)
1003
 
0.6%
502
 
0.4%
305
 
1.0%
255
 
1.0%
204
 
0.8%
152
 
0.4%
1021
4.1%
53
 
0.6%

target_level
Real number (ℝ)

High correlation  Missing 

Distinct10
Distinct (%)22.2%
Missing467
Missing (%)91.2%
Infinite0
Infinite (%)0.0%
Mean69.555556
Minimum20
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2025-10-30T18:23:15.494234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile20
Q140
median40
Q3100
95-th percentile200
Maximum200
Range180
Interquartile range (IQR)60

Descriptive statistics

Standard deviation50.506775
Coefficient of variation (CV)0.72613575
Kurtosis1.7112448
Mean69.555556
Median Absolute Deviation (MAD)20
Skewness1.5422159
Sum3130
Variance2550.9343
MonotonicityNot monotonic
2025-10-30T18:23:15.598174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
4019
 
3.7%
1006
 
1.2%
205
 
1.0%
2004
 
0.8%
503
 
0.6%
602
 
0.4%
802
 
0.4%
1202
 
0.4%
1251
 
0.2%
751
 
0.2%
(Missing)467
91.2%
ValueCountFrequency (%)
205
 
1.0%
4019
3.7%
503
 
0.6%
602
 
0.4%
751
 
0.2%
802
 
0.4%
1006
 
1.2%
1202
 
0.4%
1251
 
0.2%
2004
 
0.8%
ValueCountFrequency (%)
2004
 
0.8%
1251
 
0.2%
1202
 
0.4%
1006
 
1.2%
802
 
0.4%
751
 
0.2%
602
 
0.4%
503
 
0.6%
4019
3.7%
205
 
1.0%

quantity_per_unit
Text

Missing 

Distinct32
Distinct (%)80.0%
Missing472
Missing (%)92.2%
Memory size17.6 KiB
2025-10-30T18:23:15.745974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length20
Median length17.5
Mean length12.25
Min length4

Characters and Unicode

Total characters490
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)70.0%

Sample

1st row15.25 OZ
2nd row15.25 OZ
3rd row14.5 OZ
4th row14.5 OZ
5th row14.5 OZ
ValueCountFrequency (%)
oz18
 
12.2%
17
 
11.6%
1210
 
6.8%
pkgs8
 
5.4%
boxes8
 
5.4%
247
 
4.8%
g6
 
4.1%
15.255
 
3.4%
jars4
 
2.7%
14.53
 
2.0%
Other values (37)61
41.5%
2025-10-30T18:23:16.035274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
107
21.8%
s30
 
6.1%
229
 
5.9%
127
 
5.5%
o24
 
4.9%
522
 
4.5%
g21
 
4.3%
021
 
4.3%
b18
 
3.7%
-17
 
3.5%
Other values (23)174
35.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)490
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
107
21.8%
s30
 
6.1%
229
 
5.9%
127
 
5.5%
o24
 
4.9%
522
 
4.5%
g21
 
4.3%
021
 
4.3%
b18
 
3.7%
-17
 
3.5%
Other values (23)174
35.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)490
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
107
21.8%
s30
 
6.1%
229
 
5.9%
127
 
5.5%
o24
 
4.9%
522
 
4.5%
g21
 
4.3%
021
 
4.3%
b18
 
3.7%
-17
 
3.5%
Other values (23)174
35.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)490
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
107
21.8%
s30
 
6.1%
229
 
5.9%
127
 
5.5%
o24
 
4.9%
522
 
4.5%
g21
 
4.3%
021
 
4.3%
b18
 
3.7%
-17
 
3.5%
Other values (23)174
35.5%

discontinued
Categorical

Constant  Missing 

Distinct1
Distinct (%)2.2%
Missing467
Missing (%)91.2%
Memory size28.2 KiB
0
45 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
045
 
8.8%
(Missing)467
91.2%

Length

2025-10-30T18:23:16.158565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-30T18:23:16.240059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
045
100.0%

Most occurring characters

ValueCountFrequency (%)
045
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)45
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
045
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)45
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
045
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)45
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
045
100.0%

minimum_reorder_quantity
Real number (ℝ)

High correlation  Missing 

Distinct6
Distinct (%)20.0%
Missing482
Missing (%)94.1%
Infinite0
Infinite (%)0.0%
Mean15
Minimum5
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2025-10-30T18:23:16.304683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q110
median10
Q325
95-th percentile27.75
Maximum30
Range25
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.304548
Coefficient of variation (CV)0.55363653
Kurtosis-1.2860119
Mean15
Median Absolute Deviation (MAD)5
Skewness0.48381407
Sum450
Variance68.965517
MonotonicityIncreasing
2025-10-30T18:23:16.428605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1012
 
2.3%
257
 
1.4%
55
 
1.0%
152
 
0.4%
202
 
0.4%
302
 
0.4%
(Missing)482
94.1%
ValueCountFrequency (%)
55
1.0%
1012
2.3%
152
 
0.4%
202
 
0.4%
257
1.4%
302
 
0.4%
ValueCountFrequency (%)
302
 
0.4%
257
1.4%
202
 
0.4%
152
 
0.4%
1012
2.3%
55
1.0%

category
Categorical

High correlation  Missing 

Distinct16
Distinct (%)35.6%
Missing467
Missing (%)91.2%
Memory size28.8 KiB
Canned Fruit & Vegetables
Beverages
Dried Fruit & Nuts
Baked Goods & Mixes
Soups
Other values (11)
20 

Length

Max length31
Median length18
Mean length13.711111
Min length3

Characters and Unicode

Total characters617
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)11.1%

Sample

1st rowCanned Fruit & Vegetables
2nd rowCanned Fruit & Vegetables
3rd rowCanned Fruit & Vegetables
4th rowCanned Fruit & Vegetables
5th rowCanned Fruit & Vegetables

Common Values

ValueCountFrequency (%)
Canned Fruit & Vegetables8
 
1.6%
Beverages5
 
1.0%
Dried Fruit & Nuts5
 
1.0%
Baked Goods & Mixes4
 
0.8%
Soups3
 
0.6%
Canned Meat3
 
0.6%
Sauces3
 
0.6%
Condiments3
 
0.6%
Jams, Preserves2
 
0.4%
Cereal2
 
0.4%
Other values (6)7
 
1.4%
(Missing)467
91.2%

Length

2025-10-30T18:23:16.594512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
17
16.5%
fruit13
12.6%
canned11
10.7%
vegetables8
 
7.8%
beverages5
 
4.9%
dried5
 
4.9%
nuts5
 
4.9%
baked4
 
3.9%
goods4
 
3.9%
mixes4
 
3.9%
Other values (15)27
26.2%

Most occurring characters

ValueCountFrequency (%)
e82
13.3%
58
 
9.4%
s47
 
7.6%
a46
 
7.5%
t36
 
5.8%
n33
 
5.3%
r33
 
5.3%
d31
 
5.0%
i30
 
4.9%
u25
 
4.1%
Other values (30)196
31.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)617
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e82
13.3%
58
 
9.4%
s47
 
7.6%
a46
 
7.5%
t36
 
5.8%
n33
 
5.3%
r33
 
5.3%
d31
 
5.0%
i30
 
4.9%
u25
 
4.1%
Other values (30)196
31.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)617
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e82
13.3%
58
 
9.4%
s47
 
7.6%
a46
 
7.5%
t36
 
5.8%
n33
 
5.3%
r33
 
5.3%
d31
 
5.0%
i30
 
4.9%
u25
 
4.1%
Other values (30)196
31.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)617
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e82
13.3%
58
 
9.4%
s47
 
7.6%
a46
 
7.5%
t36
 
5.8%
n33
 
5.3%
r33
 
5.3%
d31
 
5.0%
i30
 
4.9%
u25
 
4.1%
Other values (30)196
31.8%

unit_cost
Real number (ℝ)

High correlation  Missing 

Distinct25
Distinct (%)45.5%
Missing457
Missing (%)89.3%
Infinite0
Infinite (%)0.0%
Mean17.495455
Minimum2
Maximum61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2025-10-30T18:23:16.759745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4.4
Q18
median14
Q326
95-th percentile39.3
Maximum61
Range59
Interquartile range (IQR)18

Descriptive statistics

Standard deviation13.068772
Coefficient of variation (CV)0.746981
Kurtosis2.5373064
Mean17.495455
Median Absolute Deviation (MAD)7
Skewness1.5316538
Sum962.25
Variance170.7928
MonotonicityNot monotonic
2025-10-30T18:23:16.949204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
78
 
1.6%
107
 
1.4%
344
 
0.8%
84
 
0.8%
164
 
0.8%
263
 
0.6%
143
 
0.6%
302
 
0.4%
152
 
0.4%
22
 
0.4%
Other values (15)16
 
3.1%
(Missing)457
89.3%
ValueCountFrequency (%)
22
 
0.4%
31
 
0.2%
51
 
0.2%
78
1.6%
84
0.8%
91
 
0.2%
107
1.4%
132
 
0.4%
13.51
 
0.2%
143
 
0.6%
ValueCountFrequency (%)
611
 
0.2%
601
 
0.2%
401
 
0.2%
391
 
0.2%
344
0.8%
302
0.4%
291
 
0.2%
281
 
0.2%
263
0.6%
221
 
0.2%

date_received
Date

Missing 

Distinct6
Distinct (%)14.0%
Missing469
Missing (%)91.6%
Memory size4.1 KiB
Minimum2006-01-22 00:00:00
Maximum2006-04-17 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-10-30T18:23:17.068059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-30T18:23:17.203493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)

posted_to_inventory
Categorical

High correlation  Missing 

Distinct2
Distinct (%)3.6%
Missing457
Missing (%)89.3%
Memory size28.2 KiB
1
43 
0
12 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters55
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
143
 
8.4%
012
 
2.3%
(Missing)457
89.3%

Length

2025-10-30T18:23:17.350816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-30T18:23:17.440114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
143
78.2%
012
 
21.8%

Most occurring characters

ValueCountFrequency (%)
143
78.2%
012
 
21.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)55
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
143
78.2%
012
 
21.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)55
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
143
78.2%
012
 
21.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)55
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
143
78.2%
012
 
21.8%

supplier_id
Real number (ℝ)

High correlation  Missing 

Distinct8
Distinct (%)28.6%
Missing484
Missing (%)94.5%
Infinite0
Infinite (%)0.0%
Mean3.1785714
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2025-10-30T18:23:17.521306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q35
95-th percentile7.65
Maximum8
Range7
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.3262364
Coefficient of variation (CV)0.73184965
Kurtosis-0.65458685
Mean3.1785714
Median Absolute Deviation (MAD)1
Skewness0.85097162
Sum89
Variance5.4113757
MonotonicityNot monotonic
2025-10-30T18:23:17.642373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
29
 
1.8%
18
 
1.6%
63
 
0.6%
53
 
0.6%
82
 
0.4%
41
 
0.2%
31
 
0.2%
71
 
0.2%
(Missing)484
94.5%
ValueCountFrequency (%)
18
1.6%
29
1.8%
31
 
0.2%
41
 
0.2%
53
 
0.6%
63
 
0.6%
71
 
0.2%
82
 
0.4%
ValueCountFrequency (%)
82
 
0.4%
71
 
0.2%
63
 
0.6%
53
 
0.6%
41
 
0.2%
31
 
0.2%
29
1.8%
18
1.6%

created_by
Real number (ℝ)

High correlation  Missing 

Distinct8
Distinct (%)32.0%
Missing487
Missing (%)95.1%
Infinite0
Infinite (%)0.0%
Mean3.36
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2025-10-30T18:23:17.721774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q34
95-th percentile7
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.2338308
Coefficient of variation (CV)0.66483059
Kurtosis0.29415092
Mean3.36
Median Absolute Deviation (MAD)1
Skewness1.1396509
Sum84
Variance4.99
MonotonicityNot monotonic
2025-10-30T18:23:17.822066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
211
 
2.1%
73
 
0.6%
13
 
0.6%
43
 
0.6%
32
 
0.4%
51
 
0.2%
91
 
0.2%
61
 
0.2%
(Missing)487
95.1%
ValueCountFrequency (%)
13
 
0.6%
211
2.1%
32
 
0.4%
43
 
0.6%
51
 
0.2%
61
 
0.2%
73
 
0.6%
91
 
0.2%
ValueCountFrequency (%)
91
 
0.2%
73
 
0.6%
61
 
0.2%
51
 
0.2%
43
 
0.6%
32
 
0.4%
211
2.1%
13
 
0.6%

submitted_date
Date

Missing 

Distinct7
Distinct (%)25.0%
Missing484
Missing (%)94.5%
Memory size4.1 KiB
Minimum2006-01-14 00:00:00
Maximum2006-04-26 18:33:52
Invalid dates0
Invalid dates (%)0.0%
2025-10-30T18:23:17.921685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-30T18:23:18.017014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)

creation_date
Date

Missing 

Distinct9
Distinct (%)32.1%
Missing484
Missing (%)94.5%
Memory size4.1 KiB
Minimum2006-01-22 00:00:00
Maximum2006-04-26 18:33:52
Invalid dates0
Invalid dates (%)0.0%
2025-10-30T18:23:18.106438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-30T18:23:18.221234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)

expected_date
Unsupported

Missing  Rejected  Unsupported 

Missing512
Missing (%)100.0%
Memory size4.1 KiB

payment_date
Unsupported

Missing  Rejected  Unsupported 

Missing512
Missing (%)100.0%
Memory size4.1 KiB

payment_amount
Categorical

Constant  Missing 

Distinct1
Distinct (%)3.6%
Missing484
Missing (%)94.5%
Memory size28.2 KiB
0.0
28 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters84
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.028
 
5.5%
(Missing)484
94.5%

Length

2025-10-30T18:23:18.361715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-30T18:23:18.431632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.028
100.0%

Most occurring characters

ValueCountFrequency (%)
056
66.7%
.28
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)84
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
056
66.7%
.28
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)84
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
056
66.7%
.28
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)84
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
056
66.7%
.28
33.3%

payment_method
Text

Constant  Missing 

Distinct1
Distinct (%)50.0%
Missing510
Missing (%)99.6%
Memory size16.2 KiB
2025-10-30T18:23:18.474106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters10
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCheck
2nd rowCheck
ValueCountFrequency (%)
check2
100.0%
2025-10-30T18:23:18.633457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C2
20.0%
h2
20.0%
e2
20.0%
c2
20.0%
k2
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)10
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C2
20.0%
h2
20.0%
e2
20.0%
c2
20.0%
k2
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C2
20.0%
h2
20.0%
e2
20.0%
c2
20.0%
k2
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C2
20.0%
h2
20.0%
e2
20.0%
c2
20.0%
k2
20.0%

approved_by
Categorical

Constant  Missing 

Distinct1
Distinct (%)4.0%
Missing487
Missing (%)95.1%
Memory size28.2 KiB
2
25 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
225
 
4.9%
(Missing)487
95.1%

Length

2025-10-30T18:23:18.764585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-30T18:23:18.837589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
225
100.0%

Most occurring characters

ValueCountFrequency (%)
225
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)25
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
225
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)25
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
225
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)25
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
225
100.0%

approved_date
Date

Missing 

Distinct5
Distinct (%)20.0%
Missing487
Missing (%)95.1%
Memory size4.1 KiB
Minimum2006-01-22 00:00:00
Maximum2006-04-25 17:18:51
Invalid dates0
Invalid dates (%)0.0%
2025-10-30T18:23:18.922833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-30T18:23:19.048237image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=5)

submitted_by
Real number (ℝ)

High correlation  Missing 

Distinct8
Distinct (%)28.6%
Missing484
Missing (%)94.5%
Infinite0
Infinite (%)0.0%
Mean3.2142857
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2025-10-30T18:23:19.129793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q34
95-th percentile7
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.149197
Coefficient of variation (CV)0.66863906
Kurtosis0.77914337
Mean3.2142857
Median Absolute Deviation (MAD)0.5
Skewness1.3160464
Sum90
Variance4.6190476
MonotonicityNot monotonic
2025-10-30T18:23:19.201604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
214
 
2.7%
73
 
0.6%
13
 
0.6%
43
 
0.6%
32
 
0.4%
51
 
0.2%
91
 
0.2%
61
 
0.2%
(Missing)484
94.5%
ValueCountFrequency (%)
13
 
0.6%
214
2.7%
32
 
0.4%
43
 
0.6%
51
 
0.2%
61
 
0.2%
73
 
0.6%
91
 
0.2%
ValueCountFrequency (%)
91
 
0.2%
73
 
0.6%
61
 
0.2%
51
 
0.2%
43
 
0.6%
32
 
0.4%
214
2.7%
13
 
0.6%

group_by
Text

Missing 

Distinct5
Distinct (%)100.0%
Missing507
Missing (%)99.0%
Memory size16.3 KiB
2025-10-30T18:23:19.278554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length14
Median length11
Mean length10.8
Min length8

Characters and Unicode

Total characters54
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)100.0%

Sample

1st rowcountry_region
2nd rowCustomer ID
3rd rowemployee_id
4th rowCategory
5th rowProduct ID
ValueCountFrequency (%)
id2
28.6%
country_region1
14.3%
customer1
14.3%
employee_id1
14.3%
category1
14.3%
product1
14.3%
2025-10-30T18:23:19.416891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o6
 
11.1%
e6
 
11.1%
r5
 
9.3%
t4
 
7.4%
y3
 
5.6%
u3
 
5.6%
n2
 
3.7%
c2
 
3.7%
_2
 
3.7%
g2
 
3.7%
Other values (12)19
35.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)54
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o6
 
11.1%
e6
 
11.1%
r5
 
9.3%
t4
 
7.4%
y3
 
5.6%
u3
 
5.6%
n2
 
3.7%
c2
 
3.7%
_2
 
3.7%
g2
 
3.7%
Other values (12)19
35.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)54
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o6
 
11.1%
e6
 
11.1%
r5
 
9.3%
t4
 
7.4%
y3
 
5.6%
u3
 
5.6%
n2
 
3.7%
c2
 
3.7%
_2
 
3.7%
g2
 
3.7%
Other values (12)19
35.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)54
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o6
 
11.1%
e6
 
11.1%
r5
 
9.3%
t4
 
7.4%
y3
 
5.6%
u3
 
5.6%
n2
 
3.7%
c2
 
3.7%
_2
 
3.7%
g2
 
3.7%
Other values (12)19
35.2%

display
Text

Missing 

Distinct5
Distinct (%)100.0%
Missing507
Missing (%)99.0%
Memory size16.3 KiB
2025-10-30T18:23:19.476268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length14
Median length8
Mean length9
Min length7

Characters and Unicode

Total characters45
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)100.0%

Sample

1st rowCountry/Region
2nd rowCustomer
3rd rowEmployee
4th rowCategory
5th rowProduct
ValueCountFrequency (%)
country/region1
20.0%
customer1
20.0%
employee1
20.0%
category1
20.0%
product1
20.0%
2025-10-30T18:23:19.729500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o6
13.3%
e5
11.1%
t4
 
8.9%
r4
 
8.9%
C3
 
6.7%
y3
 
6.7%
u3
 
6.7%
n2
 
4.4%
g2
 
4.4%
m2
 
4.4%
Other values (11)11
24.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)45
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o6
13.3%
e5
11.1%
t4
 
8.9%
r4
 
8.9%
C3
 
6.7%
y3
 
6.7%
u3
 
6.7%
n2
 
4.4%
g2
 
4.4%
m2
 
4.4%
Other values (11)11
24.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)45
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o6
13.3%
e5
11.1%
t4
 
8.9%
r4
 
8.9%
C3
 
6.7%
y3
 
6.7%
u3
 
6.7%
n2
 
4.4%
g2
 
4.4%
m2
 
4.4%
Other values (11)11
24.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)45
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o6
13.3%
e5
11.1%
t4
 
8.9%
r4
 
8.9%
C3
 
6.7%
y3
 
6.7%
u3
 
6.7%
n2
 
4.4%
g2
 
4.4%
m2
 
4.4%
Other values (11)11
24.4%

title
Text

Missing 

Distinct5
Distinct (%)100.0%
Missing507
Missing (%)99.0%
Memory size16.3 KiB
2025-10-30T18:23:19.877563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length17
Median length17
Mean length16.6
Min length16

Characters and Unicode

Total characters83
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)100.0%

Sample

1st rowSales By Country
2nd rowSales By Customer
3rd rowSales By Employee
4th rowSales By Category
5th rowSales by Product
ValueCountFrequency (%)
sales5
33.3%
by5
33.3%
country1
 
6.7%
customer1
 
6.7%
employee1
 
6.7%
category1
 
6.7%
product1
 
6.7%
2025-10-30T18:23:20.270753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
10
12.0%
e9
10.8%
y8
 
9.6%
l6
 
7.2%
s6
 
7.2%
a6
 
7.2%
S5
 
6.0%
o5
 
6.0%
B4
 
4.8%
r4
 
4.8%
Other values (12)20
24.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)83
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
10
12.0%
e9
10.8%
y8
 
9.6%
l6
 
7.2%
s6
 
7.2%
a6
 
7.2%
S5
 
6.0%
o5
 
6.0%
B4
 
4.8%
r4
 
4.8%
Other values (12)20
24.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)83
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
10
12.0%
e9
10.8%
y8
 
9.6%
l6
 
7.2%
s6
 
7.2%
a6
 
7.2%
S5
 
6.0%
o5
 
6.0%
B4
 
4.8%
r4
 
4.8%
Other values (12)20
24.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)83
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
10
12.0%
e9
10.8%
y8
 
9.6%
l6
 
7.2%
s6
 
7.2%
a6
 
7.2%
S5
 
6.0%
o5
 
6.0%
B4
 
4.8%
r4
 
4.8%
Other values (12)20
24.1%

filter_row_source
Text

Missing 

Distinct5
Distinct (%)100.0%
Missing507
Missing (%)99.0%
Memory size16.7 KiB
2025-10-30T18:23:20.373460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length100
Median length85
Mean length84.8
Min length71

Characters and Unicode

Total characters424
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)100.0%

Sample

1st rowSELECT DISTINCT [country_region] FROM [customers Extended] ORDER BY [country_region];
2nd rowSELECT DISTINCT [Company] FROM [customers Extended] ORDER BY [Company];
3rd rowSELECT DISTINCT [Employee Name] FROM [`dl_northwind`.`employees` Extended] ORDER BY [Employee Name];
4th rowSELECT DISTINCT [Category] FROM [`dl_northwind`.`products`] ORDER BY [Category];
5th rowSELECT DISTINCT [Product Name] FROM [`dl_northwind`.`products`] ORDER BY [Product Name];
ValueCountFrequency (%)
select5
10.6%
distinct5
10.6%
from5
10.6%
order5
10.6%
by5
10.6%
name4
8.5%
extended3
 
6.4%
customers2
 
4.3%
country_region2
 
4.3%
company2
 
4.3%
Other values (5)9
19.1%
2025-10-30T18:23:20.532793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
42
 
9.9%
e23
 
5.4%
E20
 
4.7%
o20
 
4.7%
t16
 
3.8%
d16
 
3.8%
T15
 
3.5%
R15
 
3.5%
r15
 
3.5%
[15
 
3.5%
Other values (31)227
53.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)424
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
42
 
9.9%
e23
 
5.4%
E20
 
4.7%
o20
 
4.7%
t16
 
3.8%
d16
 
3.8%
T15
 
3.5%
R15
 
3.5%
r15
 
3.5%
[15
 
3.5%
Other values (31)227
53.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)424
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
42
 
9.9%
e23
 
5.4%
E20
 
4.7%
o20
 
4.7%
t16
 
3.8%
d16
 
3.8%
T15
 
3.5%
R15
 
3.5%
r15
 
3.5%
[15
 
3.5%
Other values (31)227
53.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)424
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
42
 
9.9%
e23
 
5.4%
E20
 
4.7%
o20
 
4.7%
t16
 
3.8%
d16
 
3.8%
T15
 
3.5%
R15
 
3.5%
r15
 
3.5%
[15
 
3.5%
Other values (31)227
53.5%

default
Categorical

High correlation  Missing 

Distinct2
Distinct (%)40.0%
Missing507
Missing (%)99.0%
Memory size28.1 KiB
0
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)20.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
04
 
0.8%
11
 
0.2%
(Missing)507
99.0%

Length

2025-10-30T18:23:20.606364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-30T18:23:20.671146image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
04
80.0%
11
 
20.0%

Most occurring characters

ValueCountFrequency (%)
04
80.0%
11
 
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)5
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
04
80.0%
11
 
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
04
80.0%
11
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
04
80.0%
11
 
20.0%

string_id
Real number (ℝ)

High correlation  Missing 

Distinct62
Distinct (%)100.0%
Missing450
Missing (%)87.9%
Infinite0
Infinite (%)0.0%
Mean45.306452
Minimum2
Maximum114
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2025-10-30T18:23:20.746126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5.05
Q117.25
median33.5
Q348.75
95-th percentile110.95
Maximum114
Range112
Interquartile range (IQR)31.5

Descriptive statistics

Standard deviation37.279005
Coefficient of variation (CV)0.82281891
Kurtosis-0.76007804
Mean45.306452
Median Absolute Deviation (MAD)16
Skewness0.8729872
Sum2809
Variance1389.7242
MonotonicityNot monotonic
2025-10-30T18:23:20.850503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61
 
0.2%
41
 
0.2%
101
 
0.2%
51
 
0.2%
71
 
0.2%
31
 
0.2%
91
 
0.2%
21
 
0.2%
461
 
0.2%
241
 
0.2%
Other values (52)52
 
10.2%
(Missing)450
87.9%
ValueCountFrequency (%)
21
0.2%
31
0.2%
41
0.2%
51
0.2%
61
0.2%
71
0.2%
81
0.2%
91
0.2%
101
0.2%
111
0.2%
ValueCountFrequency (%)
1141
0.2%
1131
0.2%
1121
0.2%
1111
0.2%
1101
0.2%
1091
0.2%
1081
0.2%
1071
0.2%
1061
0.2%
1051
0.2%

string_data
Text

Missing 

Distinct62
Distinct (%)100.0%
Missing450
Missing (%)87.9%
Memory size21.0 KiB
2025-10-30T18:23:20.984657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length149
Median length73
Mean length54.822581
Min length17

Characters and Unicode

Total characters3399
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)100.0%

Sample

1st rowBack ordered product filled for Order #|
2nd rowInsufficient inventory.
3rd rowCannot remove posted inventory!
4th rowMust specify customer name!
5th rowDiscounted price below cost!
ValueCountFrequency (%)
to23
 
4.6%
order21
 
4.2%
inventory17
 
3.4%
purchase17
 
3.4%
you16
 
3.2%
product15
 
3.0%
cannot13
 
2.6%
for11
 
2.2%
must10
 
2.0%
successfully10
 
2.0%
Other values (147)350
69.6%
2025-10-30T18:23:21.235338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
451
13.3%
e343
 
10.1%
r257
 
7.6%
o249
 
7.3%
t222
 
6.5%
n194
 
5.7%
s179
 
5.3%
i166
 
4.9%
d148
 
4.4%
a147
 
4.3%
Other values (41)1043
30.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)3399
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
451
13.3%
e343
 
10.1%
r257
 
7.6%
o249
 
7.3%
t222
 
6.5%
n194
 
5.7%
s179
 
5.3%
i166
 
4.9%
d148
 
4.4%
a147
 
4.3%
Other values (41)1043
30.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3399
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
451
13.3%
e343
 
10.1%
r257
 
7.6%
o249
 
7.3%
t222
 
6.5%
n194
 
5.7%
s179
 
5.3%
i166
 
4.9%
d148
 
4.4%
a147
 
4.3%
Other values (41)1043
30.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3399
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
451
13.3%
e343
 
10.1%
r257
 
7.6%
o249
 
7.3%
t222
 
6.5%
n194
 
5.7%
s179
 
5.3%
i166
 
4.9%
d148
 
4.4%
a147
 
4.3%
Other values (41)1043
30.7%

Interactions

2025-10-30T18:22:50.959434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-10-30T18:22:50.727929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-30T18:22:52.444324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-30T18:21:53.352827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-30T18:21:55.474465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-30T18:21:57.270290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-30T18:21:59.484835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-30T18:22:02.088492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-30T18:22:03.877064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-30T18:22:06.708777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-30T18:22:10.431962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-30T18:22:12.868092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-30T18:22:16.400903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-30T18:22:20.825717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-30T18:22:25.468851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-30T18:22:29.611307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-30T18:22:34.107953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-30T18:22:37.339267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-30T18:22:41.528315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-30T18:22:45.354635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-30T18:22:48.605852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-30T18:22:50.825998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-30T18:22:52.527242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-30T18:21:53.451611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-30T18:21:55.602230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-30T18:21:57.358069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-30T18:21:59.606941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-30T18:22:02.182767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-30T18:22:03.953525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-30T18:22:06.908830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-30T18:22:10.569644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-30T18:22:12.984159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-30T18:22:16.544044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-30T18:22:21.021266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-30T18:22:25.615538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-30T18:22:29.782360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-30T18:22:34.292222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-30T18:22:37.682863image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-30T18:22:41.814244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-30T18:22:45.492688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-30T18:22:48.776453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-30T18:22:50.900066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-10-30T18:23:21.366352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
__table_name__categorycitycreated_bycustomer_iddefaultemployee_idfax_numberidinventory_idjob_titlelist_priceminimum_reorder_quantityorder_idpayment_typeposted_to_inventoryproduct_idpurchase_order_idquantityreorder_levelship_addressship_cityship_nameship_state_provinceshipper_idshipping_feestandard_coststate_provincestatus_idstring_idsubmitted_bysupplier_idsupplier_idstarget_leveltransaction_typeunit_costunit_priceweb_page
__table_name__1.0001.0000.4781.0001.0001.0000.1960.9260.5390.6990.7401.0001.0000.0001.0001.0000.0000.5260.1491.0001.0001.0001.0001.0001.0000.3221.0000.5390.6701.0001.0001.0001.0001.0001.0001.0001.0001.000
category1.0001.0000.0000.0000.0000.0000.0000.0000.3780.0000.0000.1070.2770.0000.0000.0000.0000.0000.0000.3670.0000.0000.0000.0000.0000.0000.2310.0000.0000.0000.0000.0000.7090.3920.0000.0000.0000.000
city0.4780.0001.0000.0000.0000.0000.0000.5351.0000.0000.1750.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.9230.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
created_by1.0000.0000.0001.000NaN0.000NaN0.0000.067NaN0.000NaNNaNNaN0.0000.000NaNNaNNaNNaN0.0000.0000.0000.0000.000NaNNaN0.0000.000NaN1.0000.0110.000NaN0.000NaNNaN0.000
customer_id1.0000.0000.000NaN1.0000.0000.0910.000-0.039NaN0.000NaNNaNNaN0.6370.000NaNNaNNaNNaN0.8860.7820.8860.7820.0000.411NaN0.0000.066NaNNaNNaN0.000NaN0.000NaNNaN0.000
default1.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
employee_id0.1960.0000.000NaN0.0910.0001.0000.000-0.160NaN0.000NaNNaNNaN0.2620.000NaNNaNNaNNaN0.4930.4640.4930.4640.4530.493NaN0.0000.000NaNNaNNaN0.000NaN0.000NaNNaN0.000
fax_number0.9260.0000.5350.0000.0000.0000.0001.0001.0000.0000.8850.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.5610.0000.0000.0000.0000.0000.0000.0000.0000.0001.000
id0.5390.3781.0000.067-0.0390.000-0.1601.0001.000-0.1701.000-0.7430.0270.5060.0000.479-0.0250.2700.1100.3050.0000.0000.0000.0000.000-0.612-0.7551.0000.263NaN-0.0410.2780.2870.1810.500-0.0280.1151.000
inventory_id0.6990.0000.000NaNNaN0.000NaN0.000-0.1701.0000.000NaNNaN0.8910.0001.000-0.0410.737-0.217NaN0.0000.0000.0000.0000.000NaNNaN0.0000.459NaNNaNNaN0.000NaN0.000-0.1530.1580.000
job_title0.7400.0000.1750.0000.0000.0000.0000.8851.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
list_price1.0000.1070.000NaNNaN0.000NaN0.000-0.743NaN0.0001.000-0.017NaN0.0000.000NaNNaNNaN-0.1700.0000.0000.0000.0000.000NaN0.9840.0000.000NaNNaNNaN0.428-0.1120.000NaNNaN0.000
minimum_reorder_quantity1.0000.2770.000NaNNaN0.000NaN0.0000.027NaN0.000-0.0171.000NaN0.0000.000NaNNaNNaN0.9560.0000.0000.0000.0000.000NaN-0.0130.0000.000NaNNaNNaN0.1630.9870.000NaNNaN0.000
order_id0.0000.0000.000NaNNaN0.000NaN0.0000.5060.8910.000NaNNaN1.0000.0000.0000.0100.998-0.073NaN0.0000.0000.0000.0000.000NaNNaN0.0000.243NaNNaNNaN0.000NaN0.000NaN0.1600.000
payment_type1.0000.0000.0000.0000.6370.0000.2620.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.6720.5300.6720.5300.1200.2970.0000.0000.0860.0000.0000.0000.0000.0000.0000.0000.0000.000
posted_to_inventory1.0000.0000.0000.0000.0000.0000.0000.0000.4791.0000.0000.0000.0000.0000.0001.0000.0000.6740.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
product_id0.0000.0000.000NaNNaN0.000NaN0.000-0.025-0.0410.000NaNNaN0.0100.0000.0001.0000.1850.208NaN0.0000.0000.0000.0000.000NaNNaN0.0000.182NaNNaNNaN0.000NaN0.075-0.110-0.3350.000
purchase_order_id0.5260.0000.000NaNNaN0.000NaN0.0000.2700.7370.000NaNNaN0.9980.0000.6740.1851.000-0.048NaN0.0000.0000.0000.0000.000NaNNaN0.0000.000NaNNaNNaN0.000NaN0.0000.0930.1010.000
quantity0.1490.0000.000NaNNaN0.000NaN0.0000.110-0.2170.000NaNNaN-0.0730.0000.0000.208-0.0481.000NaN0.0000.0000.0000.0000.000NaNNaN0.0000.000NaNNaNNaN0.000NaN0.2380.020-0.0400.000
reorder_level1.0000.3670.000NaNNaN0.000NaN0.0000.305NaN0.000-0.1700.956NaN0.0000.000NaNNaNNaN1.0000.0000.0000.0000.0000.000NaN-0.2230.0000.000NaNNaNNaN0.2460.9380.000NaNNaN0.000
ship_address1.0000.0000.0000.0000.8860.0000.4930.0000.0000.0000.0000.0000.0000.0000.6720.0000.0000.0000.0000.0001.0000.9571.0000.9570.6110.6300.0000.0000.0420.0000.0000.0000.0000.0000.0000.0000.0000.000
ship_city1.0000.0000.0000.0000.7820.0000.4640.0000.0000.0000.0000.0000.0000.0000.5300.0000.0000.0000.0000.0000.9571.0000.9571.0000.6460.5900.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
ship_name1.0000.0000.0000.0000.8860.0000.4930.0000.0000.0000.0000.0000.0000.0000.6720.0000.0000.0000.0000.0001.0000.9571.0000.9570.6110.6300.0000.0000.0420.0000.0000.0000.0000.0000.0000.0000.0000.000
ship_state_province1.0000.0000.0000.0000.7820.0000.4640.0000.0000.0000.0000.0000.0000.0000.5300.0000.0000.0000.0000.0000.9571.0000.9571.0000.6460.5900.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
shipper_id1.0000.0000.0000.0000.0000.0000.4530.0000.0000.0000.0000.0000.0000.0000.1200.0000.0000.0000.0000.0000.6110.6460.6110.6461.0000.4110.0000.0000.1460.0000.0000.0000.0000.0000.0000.0000.0000.000
shipping_fee0.3220.0000.000NaN0.4110.0000.4930.000-0.612NaN0.000NaNNaNNaN0.2970.000NaNNaNNaNNaN0.6300.5900.6300.5900.4111.000NaN0.0000.243NaNNaNNaN0.000NaN0.000NaNNaN0.000
standard_cost1.0000.2310.000NaNNaN0.000NaN0.000-0.755NaN0.0000.984-0.013NaN0.0000.000NaNNaNNaN-0.2230.0000.0000.0000.0000.000NaN1.0000.0000.000NaNNaNNaN0.427-0.1530.000NaNNaN0.000
state_province0.5390.0000.9230.0000.0000.0000.0000.5611.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.000
status_id0.6700.0000.0000.0000.0660.0000.0000.0000.2630.4590.0000.0000.0000.2430.0860.0000.1820.0000.0000.0000.0420.0000.0420.0000.1460.2430.0000.0001.0000.0000.0000.4080.0000.0000.0000.0000.0000.000
string_id1.0000.0000.000NaNNaN0.000NaN0.000NaNNaN0.000NaNNaNNaN0.0000.000NaNNaNNaNNaN0.0000.0000.0000.0000.000NaNNaN0.0000.0001.000NaNNaN0.000NaN0.000NaNNaN0.000
submitted_by1.0000.0000.0001.000NaN0.000NaN0.000-0.041NaN0.000NaNNaNNaN0.0000.000NaNNaNNaNNaN0.0000.0000.0000.0000.000NaNNaN0.0000.000NaN1.000-0.0960.000NaN0.000NaNNaN0.000
supplier_id1.0000.0000.0000.011NaN0.000NaN0.0000.278NaN0.000NaNNaNNaN0.0000.000NaNNaNNaNNaN0.0000.0000.0000.0000.000NaNNaN0.0000.408NaN-0.0961.0000.000NaN0.000NaNNaN0.000
supplier_ids1.0000.7090.0000.0000.0000.0000.0000.0000.2870.0000.0000.4280.1630.0000.0000.0000.0000.0000.0000.2460.0000.0000.0000.0000.0000.0000.4270.0000.0000.0000.0000.0001.0000.2980.0000.0000.0000.000
target_level1.0000.3920.000NaNNaN0.000NaN0.0000.181NaN0.000-0.1120.987NaN0.0000.000NaNNaNNaN0.9380.0000.0000.0000.0000.000NaN-0.1530.0000.000NaNNaNNaN0.2981.0000.000NaNNaN0.000
transaction_type1.0000.0000.0000.0000.0000.0000.0000.0000.5000.0000.0000.0000.0000.0000.0000.0000.0750.0000.2380.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.000
unit_cost1.0000.0000.000NaNNaN0.000NaN0.000-0.028-0.1530.000NaNNaNNaN0.0000.000-0.1100.0930.020NaN0.0000.0000.0000.0000.000NaNNaN0.0000.000NaNNaNNaN0.000NaN0.0001.000NaN0.000
unit_price1.0000.0000.000NaNNaN0.000NaN0.0000.1150.1580.000NaNNaN0.1600.0000.000-0.3350.101-0.040NaN0.0000.0000.0000.0000.000NaNNaN0.0000.000NaNNaNNaN0.000NaN0.000NaN1.0000.000
web_page1.0000.0000.0000.0000.0000.0000.0001.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2025-10-30T18:22:52.820872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-30T18:22:53.474130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-10-30T18:22:56.296905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

employee_idprivilege_id__table_name__idcompanylast_namefirst_nameemail_addressjob_titlebusiness_phonehome_phonemobile_phonefax_numberaddresscitystate_provincezip_postal_codecountry_regionweb_pagenotesattachmentstype_nametransaction_typetransaction_created_datetransaction_modified_dateproduct_idquantitypurchase_order_idcustomer_order_idcommentsorder_idinvoice_datedue_datetaxshippingamount_dueunit_pricediscountstatus_iddate_allocatedinventory_idstatuscustomer_idorder_dateshipped_dateshipper_idship_nameship_addressship_cityship_state_provinceship_zip_postal_codeship_country_regionshipping_feetaxespayment_typepaid_datetax_ratetax_status_idstatus_nametax_status_nameprivilege_namesupplier_idsproduct_codeproduct_namedescriptionstandard_costlist_pricereorder_leveltarget_levelquantity_per_unitdiscontinuedminimum_reorder_quantitycategoryunit_costdate_receivedposted_to_inventorysupplier_idcreated_bysubmitted_datecreation_dateexpected_datepayment_datepayment_amountpayment_methodapproved_byapproved_datesubmitted_bygroup_bydisplaytitlefilter_row_sourcedefaultstring_idstring_data
022employee_privileges<NA>NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN<NA>NaTNaT<NA><NA><NA><NA>NaN<NA>NaTNaTNaNNaNNaNNaNNaN<NA>NaT<NA>NaN<NA>NaTNaT<NA>NaNNaNNaNNaNNaNNaNNaNNaNNaNNaTNaN<NA>NaNNaNNaNNaNNaNNaNNaNNaNNaN<NA><NA>NaN<NA><NA>NaNNaNNaT<NA><NA><NA>NaTNaTNaTNaTNaNNaN<NA>NaT<NA>NaNNaNNaNNaNNaN<NA>NaN
1<NA><NA>employees8Northwind TradersGiussaniLauralaura@northwindtraders.comSales Coordinator(123)555-0100(123)555-0102None(123)555-0103123 8th AvenueRedmondWA99999USAhttp://northwindtraders.com#http://northwindtraders.com/#Reads and writes French.NaN<NA>NaTNaT<NA><NA><NA><NA>NaN<NA>NaTNaTNaNNaNNaNNaNNaN<NA>NaT<NA>NaN<NA>NaTNaT<NA>NaNNaNNaNNaNNaNNaNNaNNaNNaNNaTNaN<NA>NaNNaNNaNNaNNaNNaNNaNNaNNaN<NA><NA>NaN<NA><NA>NaNNaNNaT<NA><NA><NA>NaTNaTNaTNaTNaNNaN<NA>NaT<NA>NaNNaNNaNNaNNaN<NA>NaN
2<NA><NA>employees5Northwind TradersThorpeStevensteven@northwindtraders.comSales Manager(123)555-0100(123)555-0102None(123)555-0103123 5th AvenueSeattleWA99999USAhttp://northwindtraders.com#http://northwindtraders.com/#Joined the company as a sales representative and was promoted to sales manager. Fluent in French.NaN<NA>NaTNaT<NA><NA><NA><NA>NaN<NA>NaTNaTNaNNaNNaNNaNNaN<NA>NaT<NA>NaN<NA>NaTNaT<NA>NaNNaNNaNNaNNaNNaNNaNNaNNaNNaTNaN<NA>NaNNaNNaNNaNNaNNaNNaNNaNNaN<NA><NA>NaN<NA><NA>NaNNaNNaT<NA><NA><NA>NaTNaTNaTNaTNaNNaN<NA>NaT<NA>NaNNaNNaNNaNNaN<NA>NaN
3<NA><NA>employees7Northwind TradersZareRobertrobert@northwindtraders.comSales Representative(123)555-0100(123)555-0102None(123)555-0103123 7th AvenueSeattleWA99999USAhttp://northwindtraders.com#http://northwindtraders.com/#NoneNaN<NA>NaTNaT<NA><NA><NA><NA>NaN<NA>NaTNaTNaNNaNNaNNaNNaN<NA>NaT<NA>NaN<NA>NaTNaT<NA>NaNNaNNaNNaNNaNNaNNaNNaNNaNNaTNaN<NA>NaNNaNNaNNaNNaNNaNNaNNaNNaN<NA><NA>NaN<NA><NA>NaNNaNNaT<NA><NA><NA>NaTNaTNaTNaTNaNNaN<NA>NaT<NA>NaNNaNNaNNaNNaN<NA>NaN
4<NA><NA>employees1Northwind TradersFreehaferNancynancy@northwindtraders.comSales Representative(123)555-0100(123)555-0102None(123)555-0103123 1st AvenueSeattleWA99999USA#http://northwindtraders.com#NoneNaN<NA>NaTNaT<NA><NA><NA><NA>NaN<NA>NaTNaTNaNNaNNaNNaNNaN<NA>NaT<NA>NaN<NA>NaTNaT<NA>NaNNaNNaNNaNNaNNaNNaNNaNNaNNaTNaN<NA>NaNNaNNaNNaNNaNNaNNaNNaNNaN<NA><NA>NaN<NA><NA>NaNNaNNaT<NA><NA><NA>NaTNaTNaTNaTNaNNaN<NA>NaT<NA>NaNNaNNaNNaNNaN<NA>NaN
5<NA><NA>employees6Northwind TradersNeipperMichaelmichael@northwindtraders.comSales Representative(123)555-0100(123)555-0102None(123)555-0103123 6th AvenueRedmondWA99999USAhttp://northwindtraders.com#http://northwindtraders.com/#Fluent in Japanese and can read and write French, Portuguese, and Spanish.NaN<NA>NaTNaT<NA><NA><NA><NA>NaN<NA>NaTNaTNaNNaNNaNNaNNaN<NA>NaT<NA>NaN<NA>NaTNaT<NA>NaNNaNNaNNaNNaNNaNNaNNaNNaNNaTNaN<NA>NaNNaNNaNNaNNaNNaNNaNNaNNaN<NA><NA>NaN<NA><NA>NaNNaNNaT<NA><NA><NA>NaTNaTNaTNaTNaNNaN<NA>NaT<NA>NaNNaNNaNNaNNaN<NA>NaN
6<NA><NA>employees3Northwind TradersKotasJanjan@northwindtraders.comSales Representative(123)555-0100(123)555-0102None(123)555-0103123 3rd AvenueRedmondWA99999USAhttp://northwindtraders.com#http://northwindtraders.com/#Was hired as a sales associate and was promoted to sales representative.NaN<NA>NaTNaT<NA><NA><NA><NA>NaN<NA>NaTNaTNaNNaNNaNNaNNaN<NA>NaT<NA>NaN<NA>NaTNaT<NA>NaNNaNNaNNaNNaNNaNNaNNaNNaNNaTNaN<NA>NaNNaNNaNNaNNaNNaNNaNNaNNaN<NA><NA>NaN<NA><NA>NaNNaNNaT<NA><NA><NA>NaTNaTNaTNaTNaNNaN<NA>NaT<NA>NaNNaNNaNNaNNaN<NA>NaN
7<NA><NA>employees4Northwind TradersSergienkoMariyamariya@northwindtraders.comSales Representative(123)555-0100(123)555-0102None(123)555-0103123 4th AvenueKirklandWA99999USAhttp://northwindtraders.com#http://northwindtraders.com/#NoneNaN<NA>NaTNaT<NA><NA><NA><NA>NaN<NA>NaTNaTNaNNaNNaNNaNNaN<NA>NaT<NA>NaN<NA>NaTNaT<NA>NaNNaNNaNNaNNaNNaNNaNNaNNaNNaTNaN<NA>NaNNaNNaNNaNNaNNaNNaNNaNNaN<NA><NA>NaN<NA><NA>NaNNaNNaT<NA><NA><NA>NaTNaTNaTNaTNaNNaN<NA>NaT<NA>NaNNaNNaNNaNNaN<NA>NaN
8<NA><NA>employees2Northwind TradersCenciniAndrewandrew@northwindtraders.comVice President, Sales(123)555-0100(123)555-0102None(123)555-0103123 2nd AvenueBellevueWA99999USAhttp://northwindtraders.com#http://northwindtraders.com/#Joined the company as a sales representative, was promoted to sales manager and was then named vice president of sales.NaN<NA>NaTNaT<NA><NA><NA><NA>NaN<NA>NaTNaTNaNNaNNaNNaNNaN<NA>NaT<NA>NaN<NA>NaTNaT<NA>NaNNaNNaNNaNNaNNaNNaNNaNNaNNaTNaN<NA>NaNNaNNaNNaNNaNNaNNaNNaNNaN<NA><NA>NaN<NA><NA>NaNNaNNaT<NA><NA><NA>NaTNaTNaTNaTNaNNaN<NA>NaT<NA>NaNNaNNaNNaNNaN<NA>NaN
9<NA><NA>employees9Northwind TradersHellung-LarsenAnneanne@northwindtraders.comSales Representative(123)555-0100(123)555-0102None(123)555-0103123 9th AvenueSeattleWA99999USAhttp://northwindtraders.com#http://northwindtraders.com/#Fluent in French and German.NaN<NA>NaTNaT<NA><NA><NA><NA>NaN<NA>NaTNaTNaNNaNNaNNaNNaN<NA>NaT<NA>NaN<NA>NaTNaT<NA>NaNNaNNaNNaNNaNNaNNaNNaNNaNNaTNaN<NA>NaNNaNNaNNaNNaNNaNNaNNaNNaN<NA><NA>NaN<NA><NA>NaNNaNNaT<NA><NA><NA>NaTNaTNaTNaTNaNNaN<NA>NaT<NA>NaNNaNNaNNaNNaN<NA>NaN
employee_idprivilege_id__table_name__idcompanylast_namefirst_nameemail_addressjob_titlebusiness_phonehome_phonemobile_phonefax_numberaddresscitystate_provincezip_postal_codecountry_regionweb_pagenotesattachmentstype_nametransaction_typetransaction_created_datetransaction_modified_dateproduct_idquantitypurchase_order_idcustomer_order_idcommentsorder_idinvoice_datedue_datetaxshippingamount_dueunit_pricediscountstatus_iddate_allocatedinventory_idstatuscustomer_idorder_dateshipped_dateshipper_idship_nameship_addressship_cityship_state_provinceship_zip_postal_codeship_country_regionshipping_feetaxespayment_typepaid_datetax_ratetax_status_idstatus_nametax_status_nameprivilege_namesupplier_idsproduct_codeproduct_namedescriptionstandard_costlist_pricereorder_leveltarget_levelquantity_per_unitdiscontinuedminimum_reorder_quantitycategoryunit_costdate_receivedposted_to_inventorysupplier_idcreated_bysubmitted_datecreation_dateexpected_datepayment_datepayment_amountpayment_methodapproved_byapproved_datesubmitted_bygroup_bydisplaytitlefilter_row_sourcedefaultstring_idstring_data
502<NA><NA>customer23Company WEntinMichaelNonePurchasing Manager(123)555-0100NoneNone(123)555-0101789 23th StreetPortlandOR99999USANoneNoneNaN<NA>NaTNaT<NA><NA><NA><NA>NaN<NA>NaTNaTNaNNaNNaNNaNNaN<NA>NaT<NA>NaN<NA>NaTNaT<NA>NaNNaNNaNNaNNaNNaNNaNNaNNaNNaTNaN<NA>NaNNaNNaNNaNNaNNaNNaNNaNNaN<NA><NA>NaN<NA><NA>NaNNaNNaT<NA><NA><NA>NaTNaTNaTNaTNaNNaN<NA>NaT<NA>NaNNaNNaNNaNNaN<NA>NaN
503<NA><NA>customer13Company MLudickAndreNonePurchasing Representative(123)555-0100NoneNone(123)555-0101456 13th StreetMemphisTN99999USANoneNoneNaN<NA>NaTNaT<NA><NA><NA><NA>NaN<NA>NaTNaTNaNNaNNaNNaNNaN<NA>NaT<NA>NaN<NA>NaTNaT<NA>NaNNaNNaNNaNNaNNaNNaNNaNNaNNaTNaN<NA>NaNNaNNaNNaNNaNNaNNaNNaNNaN<NA><NA>NaN<NA><NA>NaNNaNNaT<NA><NA><NA>NaTNaTNaTNaTNaNNaN<NA>NaT<NA>NaNNaNNaNNaNNaN<NA>NaN
504<NA><NA>customer28Company BBRaghavAmritanshNonePurchasing Manager(123)555-0100NoneNone(123)555-0101789 28th StreetMemphisTN99999USANoneNoneNaN<NA>NaTNaT<NA><NA><NA><NA>NaN<NA>NaTNaTNaNNaNNaNNaNNaN<NA>NaT<NA>NaN<NA>NaTNaT<NA>NaNNaNNaNNaNNaNNaNNaNNaNNaNNaTNaN<NA>NaNNaNNaNNaNNaNNaNNaNNaNNaN<NA><NA>NaN<NA><NA>NaNNaNNaT<NA><NA><NA>NaTNaTNaTNaTNaNNaN<NA>NaT<NA>NaNNaNNaNNaNNaN<NA>NaN
505<NA><NA>customer9Company IMortensenSvenNonePurchasing Manager(123)555-0100NoneNone(123)555-0101123 9th StreetSalt Lake CityUT99999USANoneNoneNaN<NA>NaTNaT<NA><NA><NA><NA>NaN<NA>NaTNaTNaNNaNNaNNaNNaN<NA>NaT<NA>NaN<NA>NaTNaT<NA>NaNNaNNaNNaNNaNNaNNaNNaNNaNNaTNaN<NA>NaNNaNNaNNaNNaNNaNNaNNaNNaN<NA><NA>NaN<NA><NA>NaNNaNNaT<NA><NA><NA>NaTNaTNaTNaTNaNNaN<NA>NaT<NA>NaNNaNNaNNaNNaN<NA>NaN
506<NA><NA>customer24Company XHasselbergJonasNoneOwner(123)555-0100NoneNone(123)555-0101789 24th StreetSalt Lake CityUT99999USANoneNoneNaN<NA>NaTNaT<NA><NA><NA><NA>NaN<NA>NaTNaTNaNNaNNaNNaNNaN<NA>NaT<NA>NaN<NA>NaTNaT<NA>NaNNaNNaNNaNNaNNaNNaNNaNNaNNaTNaN<NA>NaNNaNNaNNaNNaNNaNNaNNaNNaN<NA><NA>NaN<NA><NA>NaNNaNNaT<NA><NA><NA>NaTNaTNaTNaTNaNNaN<NA>NaT<NA>NaNNaNNaNNaNNaN<NA>NaN
507<NA><NA>customer1Company ABedecsAnnaNoneOwner(123)555-0100NoneNone(123)555-0101123 1st StreetSeattleWA99999USANoneNoneNaN<NA>NaTNaT<NA><NA><NA><NA>NaN<NA>NaTNaTNaNNaNNaNNaNNaN<NA>NaT<NA>NaN<NA>NaTNaT<NA>NaNNaNNaNNaNNaNNaNNaNNaNNaNNaTNaN<NA>NaNNaNNaNNaNNaNNaNNaNNaNNaN<NA><NA>NaN<NA><NA>NaNNaNNaT<NA><NA><NA>NaTNaTNaTNaTNaNNaN<NA>NaT<NA>NaNNaNNaNNaNNaN<NA>NaN
508<NA><NA>customer1Company ABedecsAnnaNoneOwner(123)555-0100NoneNone(123)555-0101123 1st StreetSeattleWA99999USANoneNoneNaN<NA>NaTNaT<NA><NA><NA><NA>NaN<NA>NaTNaTNaNNaNNaNNaNNaN<NA>NaT<NA>NaN<NA>NaTNaT<NA>NaNNaNNaNNaNNaNNaNNaNNaNNaNNaTNaN<NA>NaNNaNNaNNaNNaNNaNNaNNaNNaN<NA><NA>NaN<NA><NA>NaNNaNNaT<NA><NA><NA>NaTNaTNaTNaTNaNNaN<NA>NaT<NA>NaNNaNNaNNaNNaN<NA>NaN
509<NA><NA>customer17Company QBagelJean PhilippeNoneOwner(123)555-0100NoneNone(123)555-0101456 17th StreetSeattleWA99999USANoneNoneNaN<NA>NaTNaT<NA><NA><NA><NA>NaN<NA>NaTNaTNaNNaNNaNNaNNaN<NA>NaT<NA>NaN<NA>NaTNaT<NA>NaNNaNNaNNaNNaNNaNNaNNaNNaNNaTNaN<NA>NaNNaNNaNNaNNaNNaNNaNNaNNaN<NA><NA>NaN<NA><NA>NaNNaNNaT<NA><NA><NA>NaTNaTNaTNaTNaNNaN<NA>NaT<NA>NaNNaNNaNNaNNaN<NA>NaN
510<NA><NA>customer6Company FPérez-OlaetaFranciscoNonePurchasing Manager(123)555-0100NoneNone(123)555-0101123 6th StreetMilwaukeeWI99999USANoneNoneNaN<NA>NaTNaT<NA><NA><NA><NA>NaN<NA>NaTNaTNaNNaNNaNNaNNaN<NA>NaT<NA>NaN<NA>NaTNaT<NA>NaNNaNNaNNaNNaNNaNNaNNaNNaNNaTNaN<NA>NaNNaNNaNNaNNaNNaNNaNNaNNaN<NA><NA>NaN<NA><NA>NaNNaNNaT<NA><NA><NA>NaTNaTNaTNaTNaNNaN<NA>NaT<NA>NaNNaNNaNNaNNaN<NA>NaN
511<NA><NA>customer22Company VRamosLucianaNonePurchasing Assistant(123)555-0100NoneNone(123)555-0101789 22th StreetMilwaukeeWI99999USANoneNoneNaN<NA>NaTNaT<NA><NA><NA><NA>NaN<NA>NaTNaTNaNNaNNaNNaNNaN<NA>NaT<NA>NaN<NA>NaTNaT<NA>NaNNaNNaNNaNNaNNaNNaNNaNNaNNaTNaN<NA>NaNNaNNaNNaNNaNNaNNaNNaNNaN<NA><NA>NaN<NA><NA>NaNNaNNaT<NA><NA><NA>NaTNaTNaTNaTNaNNaN<NA>NaT<NA>NaNNaNNaNNaNNaN<NA>NaN

Duplicate rows

Most frequently occurring

employee_idprivilege_id__table_name__idcompanylast_namefirst_nameemail_addressjob_titlebusiness_phonehome_phonefax_numberaddresscitystate_provincezip_postal_codecountry_regionweb_pagenotesattachmentstype_nametransaction_typetransaction_created_datetransaction_modified_dateproduct_idquantitypurchase_order_idcommentsorder_idinvoice_datetaxshippingamount_dueunit_pricediscountstatus_idinventory_idstatuscustomer_idorder_dateshipped_dateshipper_idship_nameship_addressship_cityship_state_provinceship_zip_postal_codeship_country_regionshipping_feetaxespayment_typepaid_datetax_ratestatus_nametax_status_nameprivilege_namesupplier_idsproduct_codeproduct_namestandard_costlist_pricereorder_leveltarget_levelquantity_per_unitdiscontinuedminimum_reorder_quantitycategoryunit_costdate_receivedposted_to_inventorysupplier_idcreated_bysubmitted_datecreation_datepayment_amountpayment_methodapproved_byapproved_datesubmitted_bygroup_bydisplaytitlefilter_row_sourcedefaultstring_idstring_data# duplicates
0<NA><NA>customer1Company ABedecsAnnaNaNOwner(123)555-0100NaN(123)555-0101123 1st StreetSeattleWA99999USANaNNaNNaN<NA>NaTNaT<NA><NA><NA>NaN<NA>NaTNaNNaNNaNNaNNaN<NA><NA>NaN<NA>NaTNaT<NA>NaNNaNNaNNaNNaNNaNNaNNaNNaNNaTNaNNaNNaNNaNNaNNaNNaNNaNNaN<NA><NA>NaN<NA><NA>NaNNaNNaT<NA><NA><NA>NaTNaTNaNNaN<NA>NaT<NA>NaNNaNNaNNaNNaN<NA>NaN2